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Drivers’ rear end collision avoidance behaviors under different levels of situational urgency
... Their results showed that the braking response time was longer when the deceleration rate was smaller; conversely, the greater the deceleration rate of the leading vehicle, the shorter the reaction time of the following driver. Wang et al. [15] investigated the relationship between reaction time and driver braking behavior under different levels of urgency, three rates of deceleration (0.3, 0.5, and 0.75 g), and two different headways (1.5 and 2.5 s). Their results showed that shorter distances could produce faster reaction times, and higher deceleration rates could lead to faster reaction times than lower rates. ...
... With regard to deceleration rate, previous studies have indicated that different deceleration rates affect the braking response time of following drivers [15,19]. Wood and Zhang [27] reported that the minimum and maximum deceleration rates among 2971 natural drivers were 0.23 g and 1.09 g, respectively. ...
... In this experiment, the lowest relative approach speed between the leading and following vehicles was in the 50 km/h driving environment at a 0.25 g deceleration rate to enable observation of the longest braking response time, which was significantly different from the other experimental conditions. Similar results were reported by Wang et al. [15], where the braking response time at the 0.3 g deceleration rate was significantly lower than that at the 0.5 and 0.7 g deceleration rates. Although the driving speed environment in Wang et al.'s study was at 120 km/h, which differs from the speeds used in our study, they found a phenomenon similar to the threshold for visual recognition of angular velocity change. ...
To prevent vehicle crashes, studies have proposed the use of flashing signals (brake lights or other light indicators) to improve the driver’s response time when the leading vehicle is braking. However, there are no consistent results on the ideal flashing frequency of the brake lights. This study aimed to investigate different brake light flashing frequencies to assess their impact on braking response time. Twenty-four participants aged 25 to 30 were recruited. Two driving speed environments (50 and 80 km/h), three deceleration rates (0.25, 0.6, and 1 g), and four brake light flashing frequencies (0, 2, 4, and 7 Hz) were examined. Braking response time, average braking force, and braking response time ratio were used to evaluate the driving behavior. The results showed that the braking response time and average braking force were affected by the deceleration rate in the 50 km/h driving environment. In the 50 and 80 km/h driving environments, although there were no significant differences among the three deceleration rates, the braking response time decreased by 3–7% under the flashing brake light condition. These findings can be used as a reference for safety designs as well as future studies on driving behavior.
... Drivers' reaction time to a rear-end collision is one of the key parameters that may change according to situations (Aust et al., 2013;Li et al., 2016;Liebermann et al., 1995;Wang et al., 2016;Xue et al., 2018). Liebermann et al. (1995) reported that drivers reacted faster when the following distance got shorter. ...
... Hulst and Der (1999) further found that drivers could adjust their reaction time to the deceleration level of the leading vehicle. Wang et al. (2016) tested the effect of situational urgency on driver's rear-end collision avoidance behaviors and reported that an increased situational urgency could lead to a faster deceleration. Given the varied driver response in different situations, the collision avoidance systems should take driver's real-time response into account when evaluating the situation and making the decisions of warning or autonomous control. ...
... Different from the warning system, the autonomous collision avoidance system, as the last defensive line, needed to interfere with driver's behavior when the danger came, and thus effectively reduce the crash rates (Cicchino, 2017). In the previous studies of autonomous braking system's algorithm, researchers usually used the speed and position information of the leading vehicle to infer the safe distance (Bella & Russo, 2011;Doi, 1994;Wang et al., 2016). However, the changes in driver behavior were often unpredictable, and the variation of deceleration and reaction time would also reduce the accuracy of the autonomous emergency braking system (Xiong et al., 2019). ...
Introduction:
To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety.
Method:
By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate.
Results:
The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of -7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation.
... An extensive simulator based experiment and analysis was performed by [3] to understand driver behaviour during the event of rear-end collision. With three different levels of LV deceleration (0.3g, 0.5g and 0.75g respectively) and initial headway time (1.5s and 2.5s respectively), the performance of test subjects in the SV was studied under varying critical scenarios. ...
... A pre-dominant bias towards braking is seen in humans and this is the major reason why rear-end collisions are reported at such high rates. [3] Another reason for these crashes is the relatively high reaction and response time of the drivers. In their simulator study [3], the KPI's defined to analyze driver behaviour are shown in [3] PRT here is the Perception Response Time defined as the time elapsed between LV's deceleration initiation and SV's steer/brake onset. ...
... [3] Another reason for these crashes is the relatively high reaction and response time of the drivers. In their simulator study [3], the KPI's defined to analyze driver behaviour are shown in [3] PRT here is the Perception Response Time defined as the time elapsed between LV's deceleration initiation and SV's steer/brake onset. The reader is advised to refer to [3] for better understanding of all the KPI definitions. ...
The automotive sector has seen a rapid transition towards autonomous driving with an aim to achieve SAE Level 5 vehicles. The incessant drive for innovation has resulted in modern passenger cars equipped with a plethora of control technologies such as ABS, VSC, AFS via EPS assist, etc. all working respectively in various critical and non-critical scenarios to ensure safe and smooth driving at all times. These efforts have reduced the number of road accidents significantly over the past years, yet it was observed that the number of rear-end crashes has not decreased but has rather maintained its proportion which is about 1/3rd of all road accidents. An evasive maneuver, a limit-handling situation, involves quick steering or braking action to avoid colliding with the vehicle in-front (thus avoiding a rear-end crash). A normal human driver is not trained to drive and control a car in such demanding and high-stress-inducing tasks. This warrants the need for an autonomous controller design that can itself take over the control of the car and perform the maneuver successfully. The aim of this research was to verify this statement by designing a novel control scheme that can steer and brake simultaneously ensuring collision avoidance and passenger safety at all times. The controller designed uses the state-of-the-art Model Predictive Control (MPC) scheme such that the best possible performance can be extracted even in critical driving scenarios. A variety of simulations were performed to successfully conclude that MPC worked well and is robust in design as well.
... to account for 20-30% of all crashes and about 10% of all fatal crashes [1]. In most cases, the driver's reaction contributes decisively to the outcome of an accident [2,3]. ...
... Several subscales have been proposed to facilitate its understanding, such as ethological validity giving an analogy with observed behavior, psychological validity comparing the mechanisms and cognitive cost involved, physiological validity focusing on similarities between physiological reactions, and emotional validity based on differences between emotional states [6,7]. The very few results obtained using open-ended questionnaires of presence after a hazardous event occurs, have shown that motorcyclists tend to forget about the scientific part of the experiment and focus on not falling, which is consistent with how they behave in reality [1,8]. Also, in studies on these lines, presence has often been measured on the basis of behavioral and physiological data, using what has been called the "field truth" paradigm [9,10]. ...
... The main results of the post-hoc analysis indicated that in the case of G1R, the values recorded were lower in trial 1 than in trial 2 (� -0.9˚, p<0.05) and that G1R obtained a lower score in this respect than G1S in trial 1 (� -1.3˚, p<0.001). In addition, an interaction was observed between 'vehicle' and 'ball' (F (27,1) = 12.92, p<0.01). The Bonferroni post-hoc test showed that in G1R, the situation with the ball triggered larger steering wheel angles (avoidance maneuvers) than the situation without the ball (� +2.4˚, p<0.001. ...
More than 1.3 million people lose their lives every year in traffic accidents. Improving road safety requires designing better vehicles and investigating drivers’ abilities more closely. Driving simulators are constantly being used for this purpose, but the question which often arises as to their validity tends to be a barrier to developments in this field. Here we studied the validity of a simulator, defined as how closely users’ behavior under simulated conditions resembles their behavior on the road, based on the concept of drivers’ feeling of presence. For this purpose, the driving behavior, physiological state and declarative data of 41 drivers were tested in the Sherpa2 simulator and in a real vehicle on a track while driving at a constant speed. During each trial, drivers had to cope with an unexpected hazardous event (a one-meter diameter gym ball crossing the road right in front of the vehicle), which occurred twice. During the speed-maintenance task, the simulator showed absolute validity, in terms of the driving and physiological parameters recorded. During the first hazardous event, the physiological parameters showed that the level of arousal (Low Heart Rate/High Heart Rate ratio x10) increased up to the end of the drive. On the other hand, the drivers’ behavioral (braking) responses were 20% more frequent in the simulator than in the real vehicle, and the physiological state parameters showed that stress reactions occurred only in the real vehicle (+5 beats per minute, +2 breaths per minute and the phasic skin conductance increased by 2). In the subjects’ declarative data, several feeling of presence sub-scales were lower under simulated conditions. These results suggest that the validity of motion based simulators for testing drivers coping with hazards needs to be questioned.
... However, several studies have demonstrated that the PRT is strongly correlated with urgency which is determined based on spacing, time-tocollision (TTC), and lead vehicle deceleration in close-following conditions (Elhenawy et al., 2017;Wang et al., 2016;Wu and Lin, 2019). Yet, there is a lack of studies that investigated the PRT in low urgency or rapid transition from low to high urgency (Green, 2017a). ...
... But since the TTC assumes the uniform speeds of lead and following vehicles, tau might not be used when a lead vehicle decelerates in more urgent situations (Green, 2017b). However, some studies found that drivers applied brakes at small values of TTC (< 1 s) (DeLucia and Tharanathan, 2009;Jurecki, 2016;Wang et al., 2016). Markkula et al. (2016) also found that when TTC was longer than 5 s, the PRT between the last off-road glance to the first observable physical reaction of the driver was <1 -6 s. ...
... However, some studies found that drivers did not apply harder brake followed by gradual ease-off under the assumption of constant τ. For instance, Wang et al. (2016) found that drivers initially applied the brake moderately and then held the brake pedal. If the drivers perceived that they would not be able to avoid the hazard by moderate braking, they changed to full brake application (Wang et al., 2016). ...
Prediction of driver reaction to the lead vehicle motion based on the perception-reaction time (PRT) is critical for prediction of rear-end crash risk. This study determines PRT at various spacings in approaching and braking conditions, and examines the association of PRT and deceleration rate with crash risk. For these tasks, a total of 50 drivers’ behavior was observed in a driving simulator experiment with 4 different scenarios – reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle’s speed change, and perception of a slow-moving lead vehicle. The study tested three hypotheses of PRT including perception and reaction thresholds and the evidence accumulation framework using a visual variable (tau-inverse). It was found that the drivers neither reacted after a specific PRT from the start of perception nor reacted at a specific value of tau-inverse. Rather, the drivers generally reacted when the accumulation of evidence (tau-inverse) over time reached a threshold. It was also found that the magnitude of deceleration rate depends on the tau-inverse at the start of braking and hence, higher crash risk was associated with higher level of urgency and insufficient brake force rather than longer PRT. This study demonstrates that the evidence accumulation framework is a promising method of predicting driver reaction in approaching and braking conditions for different types of lead vehicle, and the level of urgency is important for predicting the probability of crash.
... According to statistics from the World Health Organization, road traffic crashes cause approximately 1.35 million deaths worldwide and 20 to 50 million non-fatal injuries each year [1]. Among all traffic accidents, rear-end collisions are one of the most common types, respectively accounting for about 20% and 18% of all traffic crashes in Shanghai, China, [2] and the United States [3]. As smartphone devices are becoming used more frequently, the use of a mobile phone while driving has been identified as one of the main contributing factors to rear-end accidents [4,5]. ...
... However, it has been found that the initial time headway (ITHW; the time headway at the LV brake onset) can better predict the rear-end accident risk than the mean time headway in car-following situations. This is because the mean time headway may be different from the time headway when the LV suddenly brakes; thus, the ITHW can better reflect the level of situational urgency and directly affect the rear-end accident risk [2,24]. Many previous studies have investigated the impact of mobile phone distraction on driving performance in terms of the time headway [26][27][28], whereas very little research has been conducted on the effect of the time headway on accident probability during mobile phone distracted driving, and even fewer studies have examined the effect of the ITHW. ...
... In addition, the BRT is also regarded as an important factor associated with rear-end accident risk [10,23]; however, it is unclear from the existing studies how the BRT affects rear-end accident probability. Previous studies have suggested that the behavior of the LV (e.g., the LV deceleration rate) has a very important influence on the driving performance of the subject vehicle [2,29]. Nevertheless, the effects of the behavior of the LV (e.g., the LV deceleration rate) on the rear-end accident probability in car-following situations have not been well studied. ...
The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.
... e World Health Organization (WHO) reported that the number of annual road-traffic deaths has reached 1.35 million and that road-traffic injuries have become the leading killer of people aged 5-29 years, and the burden is disproportionately borne by traffic users of developing countries [1]. A survey conducted in the Chinese city of Shanghai showed that rear-end collisions account for 20% of all crash types and 49% of elevated expressway crashes [2]. To avoid rear-end collisions, rear-end collision alarm systems have been proposed to warn or intervene in driver behaviors to avert possible collisions. ...
... erefore, the data for the remaining 360 scenarios were analyzed. In the current study, since the driving simulator was not able to directly record the PRT value, according to previous studies, it was calculated as the time between the activation of the lead vehicle's brake lamp and the initial application of pressure onto the brake pedal [2,4]. ...
... Previous studies indicated that the time headway and lead vehicle's deceleration rate affected PRT [2,38]. Along with these results, the initial time headway was found to significantly affect the PRT, and the probabilities of not braking pedal increased with the initial time headway. ...
In the development of effective rear-end collision alarm systems, understanding the factors that influence the perception response times (PRT) of drivers is important for the design of a reasonable lead time for the warning (or intervention) of likely collisions. Previous studies have proposed different approaches for examining the impact of situational or individual factors on the PRT of drivers. However, unobserved heterogeneity has not been considered and neither has a duration-modeling approach been used, resulting in a lack of accurate estimation. The purpose of the present study was to explore the effect of the driving situation and individual differences on the PRT of drivers while also considering unobserved heterogeneity. A total of 101 participants were exposed to different levels of secondarily cognitive load and situational urgency in simulated d scenarios. Several accelerated failure time (AFT) duration models, both with and without heterogeneity, were developed to model the PRT of drivers, while factors related to driving situation and individual differences were incorporated. The results indicate that influential factors include age, working memory capacity (WMC), cognitive load, and initial time headway exerted significant effects on the PRT of drivers. The hazard rate changed by 14.4%, 22.6%, and 7.5% when age, cognitive load, and initial time headway changed by one unit, respectively. Furthermore, the hazard rate decreases by more than 20% for individuals with higher WMC compared with baseline individuals. These results suggest that the AFT model that considers unobserved heterogeneity can provide a more accurate estimation of the PRT compared to other duration models. These findings can be expected to provide a further understanding of drivers’ braking behaviors, which will contribute to the development of advanced driving assistant systems as well as safety assessments of in-vehicle information and communication technologies.
... As shown in Figure 9, when the cumulative operating frequency of throttle release reached 50%, the cumulative operating frequency of braking operation only reached about 15%. At this time, the vehicle travelled to the position of about 30 m in front of the taper starting point, which showed that when different drivers faced a low urgency situation, the transition time between throttle release and braking was longer [9]. When the vehicle was at the beginning of the deceleration lane, the cumulative operating frequency of throttle release reached 85%, while the braking cumulative operating frequency was only about 50%. ...
... 190 m downstream of the taper starting point), the cumulative operating frequency of braking only reached 85%. At this time, the cumulative operating frequency of the throttle release reached almost 100%, which also showed that with the driving situation becoming increasingly urgent, drivers in different groups released the throttle and braked more frequently [9]. It is worth noting that when the cumulative frequency of braking reached 75%, the braking operations by drivers in different groups began to become consistent. ...
The deceleration lane isbefore numbers to minus signs. a critical part of the freeway, enabling vehicles to exit the expressway safely and in an orderly fashion. However, drivers are human and thus make subjective decisions while driving; as such, each driver may approach and traverse the deceleration lane differently. This variance, which can cause major traffic disruptions and collisions, can be observed – and even mitigated, as proposed herein – based on a driver's particular characteristics. To study the variances in the longitudinal vehicle positions and microscopic operating characteristics of different drivers on a freeway exit area, a field operational test involving 46 subjects was carried out to collect data on driver characteristics, vehicle motion postures, micro-driving operations, and road geometric elements. The 46 participants were observed based on experience and gender, and the mathematical statistical method was used to analyse driving differences in the deceleration lane. The results show that (1) the vehicle motion state can be divided into four operational stages on the deceleration lane: the pre-deceleration process, the dynamic adjustment process, the first braking process, and the second braking process; (2) drivers generally adopted deceleration behaviour rather than maintain uniform speed when driving in the taper; (3) about 50% of drivers braked after entering the deceleration lane; (4) male drivers and skilled drivers were more inclined to drive at a higher speed on the deceleration lane, and female drivers showed a sharp increase in braking frequency once they travelled 110–150 m downstream of the taper starting point. The results of this study provide data and insights for deceleration lane design, traffic management, and driver training. © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
... Dynamic traffic situations including THW (or following distance), time to collision (TTC), speed, lead vehicle deceleration, and lead vehicle brake light conditions have been proved to affect driver's response to a conflict as well as the crash risk [9,[31][32][33][34]. It has been commonly recognized that the situation urgency increases with the decrease of THW and TTC, and the increase of lead vehicle deceleration and speed [31,34]. ...
... Dynamic traffic situations including THW (or following distance), time to collision (TTC), speed, lead vehicle deceleration, and lead vehicle brake light conditions have been proved to affect driver's response to a conflict as well as the crash risk [9,[31][32][33][34]. It has been commonly recognized that the situation urgency increases with the decrease of THW and TTC, and the increase of lead vehicle deceleration and speed [31,34]. As a consequence, driver brakes faster because of the enhanced visual looming cue [32], and the crash risk is increased due to the increased situation urgency. ...
Abstract Human drivers conduct compensatory behaviour to counteract the increased risk while being distracted. This kind of compensation strategy should be learned for a safer and smart design of adaptive cruise control system (ACC). Hence, a simulator study was conducted, requiring performance visual, cognitive, and combined secondary tasks during a car following scenario. An increased time headway (THW) was found in all of the three distraction conditions, which confirmed that drivers compensated an extra THW to counteract the increased crash risk. Furthermore, crash probability models using binary logistic regression with random intercept were constructed where driver distraction and dynamic traffic situations were embodied as inputs. Results showed that crash risk increased with reduced THW, increased lead vehicle deceleration, and unopened brake light of the lead vehicle. Besides, visual‐related distractions increased crash risk, while pure cognitive distraction lowered crash risk in low THW (lower than 1.8 s) condition and increased crash risk in high THW (larger than 1.8 s) condition. Based on the authors' proposed models, theoretical compensation in THW to fully counteract the increased crash risk by distraction was derived, which could be used for the design of a human‐like ACC with automatic adjustment of THW setting considering driver distraction.
... However, an effective FCW system should be designed on the base of a good understanding of the collision avoidance behaviour. There is considerable literature supporting the importance of a quick response to the lead vehicle's brake and major factors that may affect drivers' brake response time, including drivers' age, gender, profession, weather condition, and so on [4][5][6][7][8][9][10][11]. As for drivers' crash avoidance behaviour after brake onset, both driving simulator studies and naturalistic studies demonstrated that drivers often reached a maximum deceleration rate [12,13] and there was a stepwise ramp-up towards the maximum deceleration [6,12,13]. ...
Considering the large proportion of rear-end collisions occurring in our daily life and the severity it may lead to, the objective of this study was to investigate the effect of situation kinematics on drivers’ rear-end collision avoidance behaviour after brake onset. A wide range of lead vehicle deceleration scenarios were designed based on driving simulator experiments to collect drivers’ deceleration behaviour data. Different from measures (e.g., speed, the lead vehicle’s deceleration et al.) often adopted in previous studies, a visual looming-based measure at different time points was calculated combined with analysis of speed and distance to quantify situation kinematics in this study. The Spearman’s nonparametric rank correlation test was firstly conducted to examine the correlation between visual looming-based metrics and related deceleration behaviour. The mixed model was performed on drivers’ brake jerk and maximum deceleration rate, while the logistic model was then performed to predict the probability of the occurrence of rear-end collisions. Spearman’s nonparametric test showed that both deceleration ramp-up and drivers’ maximum deceleration rate increase significantly as the looming traces increase faster. Results of the logistic model indicated that the probability of occurrence of a potential collision might be higher if the situation at the brake onset is quite urgent and braking is moderate. It was demonstrated that both drivers’ deceleration ramp-up and maximum deceleration rate could be highly kinematic-dependent, and visual looming, driving speed, and distance can be useful information for drivers to take relative deceleration actions.
... Consequently, there is a need to understand driving skills, and how they are affected by stresses to respond quickly that occur in normal driving. Previous research has shown that drivers' responses to a decelerating car were more acute (e.g., braking hard to a full stop) as the distance between the cars decreased (Wang et al., 2016). While these acute responses to unexpected events are crucial for avoiding collisions or minimizing damage (Harb et al., 2009), the type of response chosen by the driver might not always be the optimal one (Markkula et al., 2012). ...
In everyday driving on the road, people are often required to make fast decisions that could compromise the accuracy of choices. We present a diffusion model analysis of the adjustments drivers make to the decision process under speed-stress. Participants operated a PC-based driving simulator while performing one of two decision-making tasks that required a driving action as a response to the stimulus. In a one-choice driving task, participants were asked to drive around a lead car when its brake lights were turned on. A two-choice driving task used a brightness-discrimination task in which participants were asked to drive to the left and back behind a lead car if there were more black than white pixels in a display and to the right and back if there were more white than black pixels. Speed-stress was operationalized by instructing drivers to respond as quickly as possible and by manipulating the distance drivers were required to maintain behind the lead car. Results showed the expected speed-accuracy tradeoff; however, the cost on accuracy in the two-choice task was relatively small. The model-based analysis showed that this was achieved by lowering the decision criteria and speeding up nondecision processes without disrupting components that produce evidence for the decision process. In fact, in the one-choice task, evidence accumulation rate in the speed-stress condition was found to be higher than in the accuracy-stress condition. We concluded that drivers were able to comply with speed-stress demands with relatively safe adjustments that imposed minimal costs on the accuracy of choices.
... However, Meng et al. [21] found that trucks have a much higher probability of being involved in a rear-end crash than a car at the work zone. For the rear-end collision situation, as situational urgency increased, drivers released the accelerator and braked to maximum more quickly [22]. ...
A large amount of traffic crash investigations have shown that rear-end collisions are the main type collisions on the freeway. The purpose of this study is to investigate the rear-end collision risk on the freeway. Firstly, a new framework was proposed to develop the rear-end collision probability (RCP) model between two vehicles based on Generalized Pareto Distribution (GPD). Secondly, the freeway rear-end collision risk (F-RCR) was defined as the sum of the rear-end collision probability of each vehicle and divided into three levels which was high, median, and low rear-end collision risk. Then, different machine learning algorithms were used to model F-RCR under the condition of an unbalanced dataset. The result of the RCP model showed continuous change and can identify the dangerous condition quickly compared to the traditional models even when the speed of the leading vehicle is faster than the following vehicle. When the vehicle distribution was unbalanced on road and the speed difference between adjacent lanes and the traffic volume was large, F-RCR will increase. Multi-Layer Perceptron (MLP) was found to be more suitable for modeling F-RCR. The framework provided in this research was transferrable and can be used in the freeway proactive traffic safety management system.
... According to the National Highway Traffic Safety Administration, rear-end collisions in the United States constituted 32.5% of all crashes in 2019 [2]. In Shanghai, China, approximately 20% of all road crashes were rear-end collisions: 49% are elevated expressway collisions, and 67% are tunnel collisions [3]. erefore, rearend collisions must be prevented to improve road traffic safety. ...
Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver’s incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%.
... The value range of the velocity and the distance gap were selected based on the parameters' true distributions of Shanghai expressway system. Different deceleration values of HDV1 were set according to the literature [41]. ...
Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs’ Operational Design Domain (ODD) needs to be identified under the scenario-based testing framework. In this study, a novel testing framework aiming at identifying the Safety performance boundary (SPB) is proposed, which assures the coverage of safety-critical scenarios and compatible with the black-box feature of HAV control algorithm. A surrogate model was utilized to approximate the safety performance of HAV, and a gradient descent searching algorithm was employed to accelerate the search for SPB. For empirical analyses, a three-vehicle following scenario was adopted and the Intelligent Driver Model (IDM) was tested as a case study. The results show that only 4% of the total scenarios are required to establish a reliable surrogate model. And the gradient descent algorithm was able to establish the SPB by identifying 97.42% of collision scenarios and only false alarming 0.29% of non-collision scenarios. Furthermore, the concept of safety tolerance was proposed to measure the possibilities of boundary scenarios dropping in safety performance. The applications of helping to construct ODD and compare different control algorithms were discussed. It shows that the IDM performs better than the Wiedemann 99 (W99) model with larger ODD.
... Rear-end collision is one of the top two collision patterns on freeways in the world. According to the NHTSA (National Highway Traffic Safety Administration) traffic accident data in 2014-2018, rear-end collisions accounted for the highest proportion, reaching 40% (NHTSA, 2018) and about 10% of all fatal crashes (Wang et al., 2016a) in the United States. In China, the 2018 traffic accident data revealed that rear-end collisions accounted for the second-highest proportion of 7.95% (Institute of Highway Science, 2019). ...
Rear-end collision and side collision are two types of accidents with the highest accident rate in the world. Numerous studies have focused on rear-end accident research, but only a few constructive countermeasures are put forward. Driving risk evolution at the driver operational level before an accident is critical to collision avoidance. This paper puts forward a driver operational level identification of driving risk and graded alarm under near-crash conditions. Firstly, driving simulation is utilized to acquire the operation data of SV (subjective vehicle) under the condition of emergent deceleration of LV (leading vehicle). The kinematic model is built to characterize the law of the risk discrimination indices of SV including THW (time headway), SHW (space headway) and TTCi (the reciprocal of time to collision). The predicted results are consistent with the naturalistic driving data. Secondly, the three-dimensional distribution 'speed-spacing-TTCi' is applied to classify the risky driving state of SV. The precarious distribution is concentrated at the area where relative velocity increased to 23-40 km/h and spacing decreased to 18-30 m. Finally, based on the reaction time and braking distance reduction, the optimal external intervention is determined to be the acousto-optic way by driving simulation. For moderate drivers, a three-level alarm of 2.94 s, 1.94 s and 1.1 s is calibrated considering different driving styles and cumulative frequency curve of reaction time.
... Traffic crash is a serious problem in the current transportation system, leading to heavy casualties and economic losses [1][2]. Among all traffic crashes, rear-end crashes account for approximately 32% in the United States and 20% in Shanghai, China [3]. Crash statistics show that most rear-end crashes are caused by improper operation and delayed reaction of drivers [4][5][6]. ...
Cooperative adaptive cruise control is recognised as an effective means to reduce the rear-end collision caused by the delayed reaction and improper driving behaviours of manually driven vehicles. However, vehicle-to-vehicle communication, as the key to collect information for the cooperative adaptive cruise control system, is likely to be interrupted by malicious attacks and equipment failures. When communication interruption occurs, sensors of the adaptive cruise control system will replace vehicle-to-vehicle communication for information collection, that is, cooperative adaptive cruise control vehicles degrade to adaptive cruise control vehicles automatically, causing an increased rear-end collision risk. This study aims to investigate the safety impact of cooperative adaptive cruise control vehicles’ degradation under spatial continuous communication interruption. A mixed platoon, consisting of cooperative adaptive cruise control vehicles, adaptive cruise control vehicles, manually driven vehicles, and improved manually driven vehicles with information-sending devices, is simulated with the realistic cooperative adaptive cruise control and adaptive cruise control model, and the intelligent driver model. Different platoon forms are discussed by adjusting the quantity and position of manually driven vehicles in the mixed platoon. The results show that manually driven vehicles can effectively block the backward extension of speed fluctuation and reduce the rear-end collision risk under the spatial continuous communication interruption. Nevertheless, equipping manually driven vehicles with information-sending devices weakens the ability to defend against communication interruption. © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
... In the meantime, the surrounding drivers could better understand the vehicles adapting human-like driving patterns and make a natural interaction in the human-robot mixed traffic environment [9,10]. With the development of human-like ADAS, there raises the need for a testing and evaluation method considering human-like behavior. ...
With the rapid and wide implementation of adaptive cruise control system (ACC), the testing and evaluation method becomes an important question. Based on the human driver behavior characteristics extracted from naturalistic driving studies (NDS), this paper proposed the testing and evaluation method for ACC systems, which considers safety and human-like at the same time. Firstly, usage scenarios of ACC systems are defined and test scenarios are extracted and categorized as safety test scenarios and human-like test scenarios according to the collision likelihood. Then, the characteristic of human driving behavior is analyzed in terms of time to collision and acceleration distribution extracted from NDS. According to the dynamic parameters distribution probability, the driving behavior is divided into safe, critical, and dangerous behavior regarding safety and aggressive and normal behavior regarding human-like according to different quantiles. Then, the baselines for evaluation are designed and the weights of different scenarios are determined according to exposure frequency, resulting in a comprehensive evaluation method. Finally, an ACC system is tested in the selected test scenarios and evaluated with the proposed method. The tested vehicle finally got a safety score of 0.9496 (full score: 1) and a human-like score as fail. The results revealed the tested vehicle has a remarkably different driving pattern to human drivers, which may lead to uncomfortable ride experience and user-distrust of the system.
... Experts have researched driver decision-making for decades and have made great progress. Some researchers have studied the factors that influence driver decision-making [8]. The general factors include the traffic environment and the driver's physiology and psychology, driving skills, perceptual reaction time, etc. ...
Speed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such as the surrounding conditions and traffic flows. This study introduced the parameter of overtaking frequency (OTF) based on the state of the traffic flow on both sides of the lane to reflect the difference between the speed of a driverless automobile and its surrounding traffic to solve the above problem. In addition, a speed evaluation algorithm was proposed based on the long short-term memory (LSTM) model. To train the LSTM model, we extracted OTF as the first observation variable, and the characteristic parameters of the vehicle’s longitudinal motion and the comparison parameters with the leading vehicle were used as the second observation variables. The algorithm judged the velocity using a hierarchical method. We conducted a road test by using real vehicles and the algorithms verified the data, which showed the accuracy rate of the model is 93%. As a result, OTF is introduced as one of the observed variables that can support the accuracy of the algorithm used to judge speed.
... The experienced drivers have more field driving hours and are able to engage in braking with steering to avoid accidents, which is similar to the research results of Wang, Zhu, Chen, and Tremont (2016). However, novice drivers have less driving experience, and they tend to be more nervous when facing an emergency. ...
A leading vehicle’s sudden deceleration can lead to a rear-end collision. Due to a lack of driving experience, novice drivers have a greater tendency to be involved in these accidents. Most previous studies have examined driver response time and braking behaviors, but few researchers have focused on what experienced and novice drivers did after their feet touched the braking pedal and their hands turned the steering wheel. These braking and steering parameters are essential in understanding driver avoidance behavior during emergencies. We programmed rear-end crash risk scenarios to examine experienced and novice drivers’ behaviors thoroughly using a driving simulator. Twenty experienced and twenty five novice subjects participated in our experiments, and their braking and steering maneuvers were recorded when leading vehicles ran at 60 km/h, 80 km/h and 100 km/h. The results showed that the two groups of subjects tended to execute two kinds of maneuvers to avoid crashes: braking only (novice 33%, experienced 19%) and the combination of braking with steering (novice 22%, experienced 26%). When the novice drivers executed braking with steering, their response time and steering duration were significantly longer than those of the experienced drivers who executed braking with steering. As the speed increased, the novice drivers’ response time, maximum braking force and maximum steering angle were significantly affected. These results showed that novice drivers should brake only when the leading vehicle suddenly decelerates. The experienced drivers executed steadier maneuvers. Their risk perception time was shorter, and their maximum braking force and the maximum steering angles were smaller. The response time, braking intensity and steering wheel angle should be considered when developing rear-end collision warning systems.
... Recent research has also started looking into how drivers' responses 25 would differ in different situations. Wang et al. (2016) found that drivers' collision avoidance 26 behavior is different in terms of different levels of situational urgency (quantified by headway 27 and different lead vehicle deceleration rates. The assessment was conducted based on drivers' 28 sequential reaction in an imminent frontal collision, including drivers' response time, evasive 29 maneuver (brake only or to brake and steer), braking delays, and braking intensity, using a high-30 fidelity driving simulator. ...
The causes and the crash-generating processes of freeway rear-end (FRE) crashes are complicated. Previous studies have highlighted the many contributing factors to crash occurrences on freeways, such as traffic flow conditions, driver-following behavior, driver attention allocation, driver characteristics, the driving environment, and drivers’ interactions with surrounding vehicles, etc. Nevertheless, few studies have looked into the combined effects of these factors on FRE crash risk as a whole. This study focuses on characterizing the sequential crash generating process of the interactions between traffic flow conditions, roadway attributes, driver behavior, event attributes, and precipitating events in FRE crashes. A sequential modeling framework for modeling the sequential and combined effects on FRE crash risk was constructed by applying structural equation modeling (SEM). The Second Highway Strategic Research Program (SHRP2) Naturalistic Driving Study (NDS) data was utilized for this purpose as this data provides extensive information concerning what happened before crashes and near-crashes. A total of 17 and 433 FRE crashes and near-crashes, respectively, were included in this study. It was found that (1) FRE crashes were associated with the sequential and combined effects of those factors above; (2) certain types of speed oscillations were identified as precursors to sudden braking when vehicles ahead decelerated or stopped-and-went; and (3) many factors were identified as being associated with driver perception time and crash occurrence.
... Driving simulator is a commonly considered data source in driving behavior analysis (see, e.g., [12], [38], [39]). It is in general straightforward to extract different control operations so that individual target maneuvering actions can be analyzed. ...
Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed.
... As the car-following distance was significantly reduced under foggy conditions, the following vehicle driver had to react faster and use a larger brake force to avoid the rear-end collision. Additionally, previous studies found that the larger deceleration of leading vehicle resulted in a faster response of following vehicle driver (Lee, McGehee, Brown, & Reyes, 2002;Wang, Zhu, Chen, & Tremont, 2016), and this is consistent with the phenomenon observed in the vehicle fleet in this study. In spite of the drivers' fast and intensive brake action, the collision risk was inevitably higher in fog, which is indicated by the smaller TTC between vehicles. ...
Vehicle fleet rear-end collisions (FRECs) are an extremely fatal type of traffic collisions on freeway and they usually occur in foggy weather. This study aimed to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits on freeway. A multiuser driving simulator system was used to conduct the experiment and the driving behavior data were collected from eight participants. The experimental results showed that as the fog density increased, the length of vehicle fleet decreased significantly, and drivers tended to keep a more stable car-following distance. The fog weather and short vehicle gap prompted drivers to react faster and brake harder in respond to the leading vehicle's brake. In spite of the compensational behaviors, more FRECs were observed under heavy fog condition. Lowering speed limit can significantly reduce the FRECs under foggy conditions. As the speed limits reduced, drivers' brake response time and speed variance significantly reduced. The study also found that drivers' brake response time was negatively correlated with their positions in the fleet. Drivers in the front positions of the fleet had a longer response time than drivers in the back positions and thus were more likely to encounter collisions. The study generated a better understanding of drivers' behavioral pattern in a vehicle fleet and the patterns of vehicle fleet rear-end collisions occurrence. The findings also shed lights on the design of driver assistance system for complex driving situations such as freeway driving under adverse weather.
In vehicles with driving assistance systems, the responsibility of driving still lies with the driver. Therefore, active safety systems follow a design principle of avoiding intervention as long as possible. Decision making using this principle requires collision avoidance with surrounding objects during the evasive maneuvering of an ego vehicle. However, general decision-making methods for collision avoidance focus on preventing collisions with objects in front of the subject vehicle; the risk of collision with the following vehicle caused by emergency braking is not considered. This paper presents decision-making methods for such collision avoidance systems, wherein emergency braking and steering are considered as collision avoidance maneuvers. The risk of collision is predicted based on the braking model of a normal driver and several emergency braking strategies are designed to avoid collisions. A within-lane steering avoidance method to improve avoidance performance in small overlap collision scenarios and a general avoidance method of steering to change lanes are designed. Collisions with surrounding objects can be avoided using the designed evasive maneuvers; further, maneuvers that can be implemented at the last moment are determined. The results of computer simulations indicate an improved collision avoidance performance using the proposed methods.
In autonomous vehicles (AVs), especially in fully AVs, “drivers” perceive vehicle operation from a passenger’s perspective. This study focuses on the perspective change of drivers especially in fully AVs. To investigate the effect of change in perspective on drivers’ assessment of AVs, this study conducted a driving simulator experiment and a survey using different samples. The driving simulator experiment was a within-subjects design including 40 participants. The experimental results indicated that although AVs drive exactly the same as the drivers, 85% of these drivers had different assessments for AVs compared to driving themselves (self-AV bias). Among these biased drivers, 80% changed the direction of their assessments (e.g., satisfied with their own driving behaviors but dissatisfied with the same behaviors from the AVs). Additionally, self-AV bias increased with the level of self-evaluation of their own driving skills. The higher the level of self-evaluation, the higher the optimism bias (drivers think they drive better than AVs) of drivers. Thereafter, to show whether and how self-AV bias affects drivers’ intuitive acceptance of AVs, a survey was conducted, which included 381 valid questionnaires. The survey results showed that self-AV bias was a critical factor promoting perceived usefulness of AVs; particularly, highly self-AV-biased drivers responded more to the perceived usefulness of AVs. Moreover, drivers who could accept more risky driving behaviors of AVs compared to themselves also perceived AVs to be more useful. The findings from this study provide insights for understanding drivers’ assessment and acceptance of AVs’ driving behaviors.
Driving aggression is a major concern during car-following situations as it is closely associated with collision risk and crash severity. Despite the tendency of reckless driving actions and increased crash risk with the vehicle ahead, aggressive driver behavior remains unexplored. The present study aimed to investigate the effects of aggressive driver behavior (three driver categories: aggressive, moderately aggressive, and non-aggressive drivers) and drivers’ tendency to aggressive stimuli (due to lead vehicle (vehicle moving ahead) reckless driving) on car-following behavior. A sample of fifty-eight Indian drivers participated in this study. All the experiments were conducted using a driving simulator. The simulator design includes a lead vehicle dynamic maneuver with rapid accelerations and decelerations (at 1 m/s², 1.5 m/s², and 2 m/s²). Three longitudinal performance measures including speed variability (SPV) from LV, speed recovery time (SRCT), and time spent tailgating, were analyzed during LV acceleration stage using generalized linear models (GLM). Further, to assess collision avoidance behavior and crash risk, deceleration adjusting time (DAT) was analyzed during the LV sudden deceleration stage using Weibull Accelerated Failure Time (AFT) analysis. The findings indicate that due to their tendency to risk-raking behavior, aggressive drivers decreased their SPV and SRCT by 25 % and 18 %, respectively. The time spent tailgating increased by 107 % for aggressive drivers. The survival probabilities were decreased by 23.77 % for aggressive drivers at 3 sec DAT. The findings of the study can be used in car-following models to enhance the model performance, improve the reliability of simulation tools, and design interventions to improve safety (in automated driving).
The Tampa Hillsborough Expressway Authority Connected Vehicle Pilot Deployment (THEA CV Pilot) implemented several vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) applications on more than 1,000 private vehicles. This paper focuses on the Forward Collision Warning (FCW) application to study factors that are associated with drivers’ reactions to FCWs and to investigate if the observed driving styles derived from the data support the participants’ stated driving styles obtained from their survey responses. A panel of participants, driving in real-world traffic conditions for over two years with retrofitted CV technology and integrated FCW application, is used. The panel consists of a treatment (Human Machine Interface (HMI) enabled) and a control (HMI disabled) group. Random parameters logit and correlated grouped random parameter logit models are estimated to reveal possible associations between stated and observed driving behavior, HMI exposure, socio-demographic factors, and the response variable (drivers’ reaction to FCW). The study found an association between one measure of driving volatility, so that with increased driving volatility (proxy for driving aggressiveness), the probability of reaction to FCW declines. The study also found that the probability of reaction for drivers who received a warning (audiovisual) via HMI increased by 9.93 % compared to those who did not receive a warning.
A necessary component of any crash avoidance or stopping distance calculation is the perception-response time. Courts have excluded the testimony of experts who used generalized average response times. Throughout history, reaction time research demonstrated that response times changed based upon probability of the event and other factors. This research will examine how drivers' response times change based upon the comparative probability of the event. The goal of the research is to determine the average and distribution if response times for several common path intrusion events. After a thorough analysis of the related literature, four primary factors were found to be associated with drivers' response times when responding to path intrusion events. 1. Comparative probability of the event was correlated with the response time. 2. Measured response times were longer when the onset (i.e., start of the measurement) was earlier in the event, and shorter when the onset was later in the event 3. That the difference between brake response time (i.e., when braking occurred) and perception-response time (i.e., when hard braking or sharp steering began) was approximately 0.3 seconds. 4. For events other than very common and very rare, the coefficient of variation, which is the standard deviation divided by the mean was approximately 0.36. There was a wider dsitribution of response times for shorter BRT events such as cutoffs , sudden appearance from behind obstacles, and longer BRT events such as head-ons (SD/Mean = 0.54). This paper will identify common mistakes made by crash investigators and then describe a classical scientific approach that meets the requirements of any court and eliminates bias from the analysis. The classical approach entails comparing the pre-impact response of a driver against the range of typical responses in research when faced with the same response scenario.
In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye’s Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB’s safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety–critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB’s Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB’s jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s²) than in IP4 (6.7 m/s²), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems.
As the automobile market gradually develops towards intelligence, networking, and information-orientated, intelligent identification based on connected vehicle data becomes a key technology. Specifically, real-time crash identification using vehicle operation data can enable automotive companies to obtain timely information on the safety of user vehicle usage so that timely customer service and roadside rescue can be provided. In this paper, an accurate vehicle crash identification algorithm is developed based on machine learning techniques using electric vehicles’ operation data provided by SAIC-GM-Wuling. The point of battery disconnection is identified as a potential crash event. Data before and after the battery disconnection is retrieved for feature extraction. Two different feature extraction methods are used: one directly extracts the descriptive statistical features of various variables, and the other directly unfolds the multivariate time series data. The AdaBoost algorithm is used to classify whether a potential crash event is a real crash using the constructed features. Models trained with the two different features are fused for the final outputs. The results show that the final model is simple, effective, and has a fast inference speed. The model has an F1 score of 0.98 on testing data for crash classification, and the identified crash times are all within 10 s around the true crash times. All data and code are available at https://github.com/MeixinZhu/vehicle-crash-identification.
The aggregate properties play a major role in the structural and functional performance of various pavement layers. The present chapter provides an introduction to aggregate sources, types, quality, and different size distribution requirements for pavement layers. Further, the chapter is categorized into two broad sections. The first section offers an extensive discussion on past and presently available conventional aggregate characterization practices, limiting values, and their significance. For example, the properties associated with the aggregate source (i.e., abrasion, impact, soundness, and absorption) and consensus or shape (i.e., FAA: fine aggregate angularity; coarse aggregate angularity, flatness and elongation, and sand equivalent) are discussed. The second part of the chapter talks about the application of various image-based techniques for aggregate shape characterization and its futuristic importance for the production of quality aggregates. The Digital Image Technique (DIT)-based methods like University of Illinois Aggregate Image Analyzer (UIAIA), Aggregate Image Measurement System (AIMS), Laser-based Aggregate Analysis System (LAAS), Videographer-40 (VDG-40), and CT-Scan are discussed. Moreover, a brief overview of various algorithms associated with digital image techniques is presented. The part also includes a comparative discussion on conventional and image-based shape characterization approaches. Additionally, the futuristic application of DIT in different aspects like aggregate production, quality control, and pavement forensic investigations are outlined. Overall, it is expected that the chapter will develop a broad understanding among researchers, practitioners, aggregate producers, and pavement stakeholders on the qualitative characterization of aggregates.KeywordsAggregatePavement layersSource propertiesShape propertiesDigital image techniqueCrushing mechanisms
Metropolitan cities of India are recently promoting transit-oriented development (TOD), as a sustainable transport approach for improving urban mobility in the nation. However, TOD policies in Indian cities require a thorough diagnosis of planning parameters based on existing urban structure elements. Existing policies need to enhance and introduce priority planning strategies that adhere to long-term visions of Indian cities. The present chapter pays attention to macro-/micro planning parameters, namely size of influence zone, typology, TOD measurement tool, floor-area-ratio (FAR), building use compositions, and priority strategies that have a substantial effect on planning processes. This chapter specifies quantitative levels of urban structure to assist as a real-world guidebook. It provides a measurement tool to understand the nature of TOD-ness and to establish a standard to plan and implement TOD at neighborhood levels. Such a TOD measurement tool can help planners, policymakers, and government authorities, while investing funds on infrastructure with prior knowledge on existing levels of TOD at neighborhood level. Because without measuring the current situation of TODs, faulty decisions in investments will continue to be repeated.KeywordsTransit-oriented development planningDefinitionTOD policy in IndiaTypologyTOD scoreDelhi Metro
A significant number of fatalities along horizontal curves of rural highways necessitated the safety evaluation in the curve design. Previous studies explored safety evaluation based on several approaches such as operating speed, driver workload, alignment index, and vehicle stability. However, researchers predominantly focused on vehicle speed because it has a close resemblance to design speed and straightforward implementation in the field. The safety evaluation criteria for horizontal curves are majorly speed-based studies. Hence, this chapter represents several operating speed-based safety evaluation approaches available in the literature. It provides insight on the requirement of holistic design guidelines and appropriate safety performance measures to develop safer geometric elements for low-middle income countries like India.
The research conducted on overtaking maneuver for evaluating drivers' safety showed adverse effects of urgency on driving performance and decision making. Therefore, a driving simulator study was designed to examine driving performance of the drivers and its implication on overtaking and crash probabilities under increasing time pressure conditions. Eighty-eight participants data were analyzed in the current study. Three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) were considered for analyzing driving performance of the drivers while executing overtaking maneuvers. The driving performance was assessed using minimum time-to-line crossing and coefficient of variation in speed to dissect the safety margin adopted by the drivers while overtaking the lead vehicle. Further, minimum time-to-line crossing and coefficient of variation in speed were considered as explanatory variables to investigate their influence on overtaking and crash probabilities. Para-metric survival analysis and Generalized Linear Mixed Models (GLMM) were used to assess the driving performance, overtaking and crash probabilities. The parametric survival analysis showed that minimum time-to-line crossing reduced by 36.7% and 63.8% in LTP and HTP driving conditions , respectively. The GLMM results revealed that coefficient of variation in speed increased by 3.437% in HTP (no significant effect in LTP) as compared to NTP driving conditions. Further, the GLMM results showed that overtaking and crash probabilities decreased with increment in minimum time-to-line crossing and coefficient of variation in speed values. Additionally, it was observed that male drivers took risky decisions than female drivers. Nevertheless, the comparative analysis revealed that male drivers were less prone to crashes than female drivers. Overall, it can be inferred that the drivers take risky decisions with increment in time pressure to complete the driving task, even at the expense of their own safety which exposed them to high likelihood of crashes.
Situation assessment is crucial for intelligent vehicles, enabling detection of potential risks to dynamic and complex traffic environments. In this paper, we propose a unified framework that tackles the coupling relationships between traffic participants and quantifies the possible range of vehicle trajectory generation and the expected losses caused by risk source attributes in the driving process. We first apply the state space trajectory planning scheme based on a sampling algorithm to generate the path candidates; each feasible path is designed through a parametric cubic spline. Then, to evaluate the risk range in the driving process, we quantify the interaction of traffic participants, and employ the principle of least action to calculate the cost of each feasible path when achieving the destination. The probability distribution map, namely the possible range of driving trajectories, can be obtained based on the path cost. Furthermore, the vehicle-to-vehicle interaction is calculated based on the equivalent force, which estimates the expected accident losses. Finally, the vehicle trajectory prediction and the expected loss are combined to output the probabilistic situation assessment of intelligent vehicles. The algorithm is implemented in different scenarios and applied to the trajectory planning process. Results demonstrate that, compared with the classical situation assessment metric, the developed method can determine and accurately identify the influence range of driving risk in real-time, predict a dangerous situation earlier, and ensure the vehicle avoids obstacles in advance.
In recent years, the development and testing of autonomous driving technology have become widespread around the world. However, due to differences in perception abilities between autonomous vehicles and human drivers, the current geometric design controls for highway alignments, designed for the human driver, may not be applicable to the autonomous vehicle (AV). Few studies, however, have systematically investigated the design controls for autonomous vehicles, though we face full driving automation in the next few decades. Because the range of modern AV sensors reaches 250 m, with expected further improvements in the near future, there is a need to determine how the sensors’ perception field and perception-reaction time may affect the current road design standards developed for human drivers. This study therefore tested the feasibility of the current design controls for fully-autonomous vehicles by separately computing controls for vertical alignments and combined horizontal and vertical alignments, considering the AV’s perception abilities of perception-reaction time (PRT), sensor height, and upward angle from the horizontal. The required stopping sight distance (SSD) and minimum length of sag and crest vertical curves were derived and compared with those for human drivers. Computations for combined alignments were based on Green Book coordination guidelines: as the minimum length of horizontal curve can be used for alignments adhering to guidelines, preview sight distance (PVSD) was computed for alignments that do not. Results showed that 1) AV-based design controls on vertical curves were more tolerant than those based on human drivers; and 2) the dominating criterion of sag vertical curve design control was comfort for autonomous vehicles, versus required SSD for human drivers.
Introduction:
Driver's evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver's evasive action. Thus, this study determines the driver's evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver's evasive action time (i.e., duration of evasive action) on rear-end collision risk.
Method:
Fifty drivers' car-following behavior on a freeway was observed using a driving simulator. An SSM called "Deceleration Rate to Avoid Crash (DRAC)" and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios - Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models.
Results:
DRAC decreased with the evasive action time and DRAC was closely related to drivers' gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario.
Conclusions:
Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver's evasive action and the speed variations among different drivers.
Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21–25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.
The current study focused on analyzing steering control of the drivers during a car-following situation under increasing time pressure conditions. A driving simulator experiment was conducted on ninety-two participants to assess steering performance measures. Five different steering control measures: Variability in Steering Angle (VSA), Steering Reversal Rate (SRR), Steering Speed (SS), Stability of Steering Control (SSC), and Maximum Steering Swerve (MSS) were examined under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. Repeated measures ANOVA (for continuous data) and Friedman's test (count data) with post-hoc analysis and Generalized Estimating Equation (GEE) modeling technique were used to investigate the influence of time pressure and different predictor variables. The statistical analysis showed that time pressure driving conditions significantly affected steering control of the drivers. The pairwise comparison of time pressure conditions revealed that HTP significantly affected most of the steering control measures as compared to LTP. Further, a GEE model also exhibited similar results where steering control measures were substantially influenced by HTP as compared to LTP. Moreover, in addition to time pressure conditions, demographic characteristics showed significant influence on steering control measures. The GEE model results showed that female drivers performed 13% more steering corrections (5°SRR) which led to better SSC by 124.44% than male drivers. Additionally, it was discovered that young-aged and experienced drivers took extra steering efforts to control lateral position of the vehicle by increasing 53.50% SS and 1% SRR compared to middle-aged and inexperienced drivers. The findings from the current study revealed that drivers undergo fast and abrupt steering maneuvers under time pressure conditions. The research approach demonstrated in the current study can be beneficial to discriminate minimum requirement of steering efforts and setup threshold values for various steering evasion techniques to control and maintain safe lateral position during car-following maneuvers.
This study aims at modelling drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for Autonomous Vehicles (AV). Several car-following behavioral measures at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) were extracted to train the model using three machine learning techniques. The results showed that the XGBoost model is superior when compared to other techniques with R-squared values of 0.99 and 0.97 for training and testing datasets. The results also indicated that the control type impact driver speed at the minimum following distance. The modelled speed will provide more comfortable speed to the AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and its control type.
Analyses of driver response time studies and fatal crash statistics were examined to determine: 1. whether all rear-end crash types can be analyzed as one crash type, 2. average braking thresholds for drivers, and 3. the influence cell phone usage has on drivers' response times when responding to a lead vehicle. The goal of this research is to recommend protocols for investigating LV crashes that is supported by the literature. Two distinct lead vehicle [LV] response time events emerged: LV platoon (two vehicles traveling together in close proximity) and LV looming (a vehicle approaching a stopped or much slower LV). In normal driving, platoon LV events were very common but resulted in very few crashes per exposure. Young drivers were over-represented when they did occur. Onset of the hazardous event was when the LV decelerated , and drivers began braking roughly 3 to 5 seconds before impact. These results were consistent with the braking thresholds by naturalistic drivers in congested traffic, as well as the GM-Ford Crash Avoidance Metrics Partnership [CAMP] test-track research. Conversely, LV looming crashes were rare, had much greater crash risk, involved drivers of all ages, and accounted for most fatal rear-end crashes. In this type of crash, drivers typically applied the brake less than 1.4 seconds before impact. These results were consistent with the braking thresholds by naturalistic drivers in smooth traffic and was nearly 3 times later than the braking thresholds by the 77th and 85th percentile responders in the CAMP tests. Hands-free and hand-held calling was not associated with an increase in brake response times when responding to a lead vehicle. However, visual and manual tasks, such as texting and dialing caused drivers to respond slower than approximately 93% of other drivers when responding to a lead vehicle. Based upon these results, a refined definition of driver distraction was offered.
During car-following, drivers respond to the braking or deceleration of a leading vehicle based on their perceived threshold for gap, relative speed, change of the gap, time-to-collision, etc. These measures are widely used to estimate drivers’ response time to the braking of a vehicle ahead. However, it is not clear if their response is driven only by absolute thresholds or also through a comparison of the current situation with any baseline situation to which they have just been exposed. This research explored drivers’ braking response to a lead vehicle’s braking using naturalistic driving data. Two hundred and ninety-six braking events were analyzed. It was found that measures adjusted from a baseline (when the lead vehicle’s brake lights were illuminated) were more important for estimating drivers’ response time than the measures on the absolute thresholds. Predictors adjusted according to the baselines are suggested for better prediction of drivers’ response time.
Safety guarantees are vital to the dependability of the automated vehicle (AV), so are of primary concern to the AV industry and regulatory bodies. Responsibility-Sensitive Safety (RSS), proposed by Mobileye, is a rigorous mathematical model that defines the real-time safety distance that the AV needs to maintain in relation to surrounding vehicles, and helps the AV respond to dangerous situations. However, RSS’s performance has not yet been tested with real driving data. This study thus calibrates and evaluates the RSS model based on car-following scenarios created from safety-critical events (SCEs) detected in the Shanghai Naturalistic Driving Study. SCEs were identified from the raw data with a trigger method; they were manually validated and then converted into virtual scenarios in which the human-driven subject vehicle was replaced with an AV controlled by a model predictive control based adaptive cruise control (ACC) system embedded with RSS. The RSS model was calibrated with the goal of achieving optimal safety performance, that is, of generating the lowest time-integrated time-to-collision (TIT) after the AV algorithms were implemented in the safety-critical scenarios. By comparing the performance of RSS-embedded ACC, ACC-only, and the unassisted human driver, this study found that: 1) RSS-embedded ACC performed best in eliminating underlying safety-critical situations by controlling the average TIT for each 15-second event at 2.65 s²; 2) RSS-embedded ACC generated the highest average relative speed and relative distance, and the lowest standard deviation of speed; and 3) on average, RSS-embedded ACC was able to perceive the SCEs 2.31 s ahead of the human driver.
Taillight shape in a vehicle provides an essential lighting signal that enables the vehicle to be seen from the rear at night, thereby preventing rear-end crashes. This study aims to investigate the effects of taillight shape on vehicle conspicuity, and proposes ergonomic taillight shape solutions to vehicle designers and manufacturers. Two complementary experiments were conducted to examine three types of taillight shapes at three design levels. The first experiment was designed to investigate the detection speed of a driver and the fixation duration and fixation counts on leading vehicles with different taillight shapes, based on an eye-tracking methodology. The second experiment was designed to investigate the dynamic visual searching performance of a trailing driver for leading vehicles with different taillight shapes, based on a visual search task. The experimental results indicated that a long line-shaped taillight (striplight) was the optimal ergonomic solution for enhancing vehicle conspicuity. Vehicles with an enclosed contour-shaped taillight were more salient than those with an open contour-shaped taillight. Moreover, the experience and gender of the driver and the vehicle–observer distance were found to be closely related to vehicle conspicuity, and therefore, must be considered by vehicle designers when applying a specific taillight shape design. This study provides insights into the taillight shape design that not only aid vehicle designers or manufacturers in enhancing vehicle safety but also enable potential vehicle buyers to choose a safe lighting system.
The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.
The forward collision warning (FCW) system is expected to reduce rear-end crashes; however, its effects on driving behavior and safety have not been thoroughly investigated, specifically the effect variations between different pre-crash scenarios. To identify these variations, this study conducted a driving simulator experiment and compared the FCW’s effects between three pre-crash scenarios: the freeway scenario, the arterial scenario and the intersection dilemma zone scenario. Thirty-nine participants were involved in the experiment. The results showed that the adaptation of driver behavior in impending rear-end collision events resulted from both the FCW and the scenario. The intersection dilemma zone scenario has indications of slowing down, which encouraged drivers to take a more aggressive response strategy under the FCW; the arterial scenario might be regarded as an “easy-to-handle” situation in which a significant portion of drivers adopted moderate level of response strategy under the FCW; both the intersection dilemma zone scenario and freeway scenario have burdened driving tasks, and this might deteriorate a driver’s ability to adapt to the FCW. In addition, different types of drivers experienced varied benefits from the FCW in each scenario. The FCW would be particularly recommended for non-experienced drivers in the freeway scenario and for female drivers in the arterial scenario; moreover, in the scenario of the intersection dilemma zone, the FCW would be particularly recommended for drivers who have a crash/citation before. The results also support specific FCW designs which are able to highlight the collision risk. This study demonstrated that it would be better to indicate the effects of the FCW under the restriction of specific scenario features and develop the FCW based on that.
This work presents a Nonlinear Model Predictive Control (NMPC) scheme to perform evasive maneuvers and avoid rear-end collisions. Rear-end collisions are among the most common road fatalities. To reduce the risk of collision, it is necessary for the controller to react as quickly as possible and exploit the full vehicle maneuverability (i.e., combined control of longitudinal and lateral dynamics). The proposed design relies on the simultaneous use of steering and braking actions to track the desired reference path and avoid collisions with the preceding vehicle. A planar vehicle model was used to describe the vehicle dynamics. In addition, the dynamics of the brake system were included in the NMPC prediction model. Furthermore, the controller incorporates constraints to ensure vehicle stability and account for actuator limitations. In this respect, the constraints were defined on Kamm circle and Ideal Brake Torque Distribution (IBD) logic for optimal tire force and brake torque distribution. To evaluate the design, the performance of the proposed NMPC was compared with two "more classical" MPC designs that rely on: (i) a linear bicycle model, and (ii) a nonlinear bicycle model. The performance of these three controller designs was evaluated in simulation (using a high-fidelity vehicle simulator) via relevant KPIs, such as reference tracking Root Mean Square (RMS) error, controller's rise/settling time, and Distance to Collision (i.e., the lateral distance by which collision was avoided safely). Different single-lane-change maneuvers were tested and the behavior of the controllers was evaluated in the presence of lateral wind disturbances, road friction variation, and maneuver aggressiveness.
This study aims at modelling unassisted drivers’ speed at the yellow onset to enhance Connected and Autonomous Vehicles applications at signalised intersections and maximise drivers’ comfort. For this purpose, a total of 2442 real-life vehicle trajectories were analysed to extract driver behavioural measures (i.e. speed, acceleration, and distance to intersection) at different times before the yellow onset. These behavioural measures were used to integrate drivers’ perceptual ability into modelling drivers’ speed at the yellow onset. To develop these models, three machine learning techniques; namely, linear regression, Support Vector Machine, and Neural Networks have been adopted. The best model was a neural network model and was selected based on the goodness-of-fit of the test dataset which has an R-squared value of 0.97. The results indicate that the speed at the yellow onset can be estimated based on behavioural measures while accounting for drivers’ perceptual ability. Also, the model can contribute to a V2I application by assisting the driver in a partially autonomous vehicle to avoid trapping in the dilemma zone and stop safely at signalised intersections. Also, the model can be used to recommend a comfortable riding speed, from a rider’s perspective to a fully autonomous vehicle.
An effective forward collision warning (FCW) system must be compatible with drivers' risk perceptions and behavioral responses. The Collision Avoidance Metrics Partnership (CAMP) developed a kinematic-based FCW algorithm to determine the minimum distance needed to stop safely under various levels of rear-end crash risk. The algorithm generates a linear function for predicting drivers' expected response decelerations (ERDs) by considering motions of the involved vehicles. This linear function works well when the risks perceived by drivers are low; however, at elevated risks when the lead vehicle (LV) decelerates at an unexpectedly high rate, or at high relative speeds, the warnings are triggered too late for the subject vehicle to avoid a rear-end collision. The current study extends the CAMP FCW algorithm to improve the handling of extreme high-collision-risk scenarios. A total of 111 brake-only noncollision events was presented in the Tongji University Driving Simulator, and drivers' braking behaviors were used to model their ERDs. We found that ERDs depended on the interaction of LV deceleration and relative speed. In response to this finding, a nonlinear function with an interaction term was combined with a linear function into a piecewise function that accommodated both higher and lower LV deceleration conditions. The applicable domain of the warning onset range was then computed for a wide range of kinematic conditions. Results showed the piecewise function to be a better predictor of ERD than the linear function, as well as to result in fewer driver rejections of the FCWs.
Collision analysis often assumes emergency deceleration begins immediately upon completion of the vehicle’s mechanical brake lag. The goal of this study is to determine the driver-related delay from initial brake application to various degrees of deceleration in a simulated emergency and to test variables contributing to the modulation of driver braking. Using the data of Mazzae et al (2003), in which drivers respond to a lateral vehicle incursion, we have assessed the contribution of Time-to-Intersection (TTI), road condition, gender and crash outcome on driver emergency brake response. In the first 0.3 second phase after initial brake application, vehicle behavior was similar across all variables as drivers reached only moderate levels of deceleration. In the second phase, drivers often took more than one second to reach emergency decelerations, especially with a longer TTI. Pavement condition, gender and crash outcome were not significant factors. We discuss the consequences of driver braking behavior in the context of driver feedback and accident reconstruction analyses.
Meta-analyses have shown that the driver braking characteristics reported in the literature often vary due to differences in study methodologies (Green, 2000; Muttart, 2005; Summala, 2000). This paper provides additional insight on driver braking performance by 1) characterizing driver behavior during the execution of surprise and expected braking maneuvers, and 2) investigating the effect of gender, age, and vehicle driven on this behavior. Sixty-four drivers performed surprise and expected braking maneuvers from 45 mph (72.4 km/h) at an inflatable barricade, and subsequently performed expected braking maneuvers in response to an auditory alarm. Participants drove one of two instrumented vehicles: 1) a 2006 Mercedes-Benz R350, or 2) a 2007 Volvo S80. Drivers' braking inputs and corrected stopping distance were measured. Results indicate that drivers' braking performance varied by gender, age, and vehicle driven. These results and their relationship to the extant braking literature are discussed. The findings are expected to benefit those involved in crash reconstruction, researchers modeling driver braking behavior, as well as designers of brake-by-wire systems.
The present experiment was carried out to explore the response of driving subjects to emergency braking. The field trial consisted of driving behind a leading vehicle while the following drivers' responses were recorded by telemetry. A group of 51 individuals performed a series of trials at two driving speeds (60 and 80km/h), two following distances (6 and 12 m), and two braking conditions (real and dummy braking). Not all of these subjects completed all conditions or the minimum number of trials. The dependent variables were the total braking time (TBT) and its subcomponents: braking reaction time (BRT), and accelerator-to-brake movement time (MT). These data were analysed in three separate three-way ANOVAs with repeated measures on all factors. The results showed that when subjects were not aware of the forthcoming braking, the distance and braking conditions had major effects on all dependent variables. At the shorter following distance drivers reacted and moved faster. Similarly, when the brakes were real compared with dummy (i.e. brake lights only) drivers reacted faster. In addition, drivers reacted to onset of brake lights in 83% of the cases when dummy braking was applied, compared with 97% when real brakes were applied. Speed of driving did not show any significant effects and did not appear to influence the cognitive or attentional set to anticipate an emergency manoeuvre. These findings suggest that changes in angular velocity during optic expansion of the leading vehicle may be used as a cue to modulate braking movement, while onset of brake lights alone may be enough to trigger a ‘ballistic’ preventive response.
In his review on driver brake reaction times (RTs), Green (2000) rightly criticizes attempts to seek a canonical brake RT, and proposes to determine expected brake RT for specific situations. However, based on his analysis, he presents a series of values for expected, unexpected, and surprise situations that appear to generalize over a variety of different driver tasks and traffic situations without sufficient concern for urgency or criticality of the situations. This sampling problem may lead easily to biased and somewhat arbitrary estimates. Thus, instead of 1.25 sec for "unexpected" situations, the median yellow response time for the critical conditions (at short time-to-stop-line) is rather below 1.0 sec, and instead of 1.5 sec mean brake RT for surprise situations, available on-road data suggest that in fairly urgent situations-at time-to-collision of about 4.0 sec-unalerted drivers are able to react to an obstacle by braking at an average latency of 1.0 to 1.3 sec, depending on site. More emphasis should be given to analyzing (and producing) real-life data on driver reactions as a function of situational and driver-centered variables, and of criticality.
Time-headway (THW) during car-following and braking response were studied in a driving simulator from the perspective that behaviour on the manoeuvring level (e.g. choice of THW) may be linked to operational competence of vehicle control (e.g. braking) via a process of adaptation. Time-headway was consistent within drivers and constant over a range of speeds. Since time-headway represents the time available to the driver to reach the same level of deceleration as the lead vehicle in case it brakes, it was studied whether choice of time-headway was related to skills underlying braking performance. The initiation and control of braking were both affected by time-to-collision (TTC) at the moment the lead vehicle started to brake. This strongly supported the idea that time-to-collision information is used for judging the moment to start braking and in the control of braking. No evidence was found that short followers differ from long followers in the ability to accurately perceive TTC. There was, however, evidence that short followers are better able to programme the intensity of braking to required levels. Also, short followers tuned the control of braking better to the development of criticality in time during the braking process. It was concluded that short followers may differ from long followers in programming and execution of the braking response.
This study investigated whether drivers who operate a vehicle equipped with a front-to-rear-end collision warning system can avoid crashing when a lead vehicle brakes at its maximum potential (e.g., -0.85 g). Drivers in the experimental condition drove a 1993 General Motors Saturn mounted on the lowa Driving Simulator's six-degree-of-freedom motion base. The simulater cab was equipped with a collision warning vehicle and the lead vehicle. Two headway distances were tested (2.7 sec and 3.2 sec). The collision avoidance performance of subject drivers was compared to the behavior of drivers in a baseline condition where no collision warning display was present. Relative to the baseline condition, results indicate that drivers using the collision warning display (a) showed significantly fewer crashes in the shorter headway condition, (b) collided with the lead vehicle at significantly slower impact speeds, (c) released the accelerator significantly faster, and (d) had longer headways both at accelerator release and brake initiation.
To improve vehicle path-following performance and reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error and steering angle as the inputs and a driver’s expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the
level of sensitivity to errors, i.e., low, middle and high level. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver’s path-following characteristics, reducing the driver workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in Carsim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, compared to the classical control strategy of steering ratio, can improve task performance by about 7%, and reduce the driver’s physical workload and mental workload by about 35% and 50%, respectively, when following the given path.
Drivers’ last-second braking and last-second steering judgments have been studied extensively by the Crash Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) Requirements project. This previous work was conducted under closed-course conditions using a realistic surrogate target lead vehicle. In the current research, a subset of these tests involving more than 4000 individual test runs has been replicated in the National Advanced Driving Simulator (NADS) facility for comparison purposes.
The major conclusions from this research are as follows:
• Scenarios need to pay careful attention to ensure initial headway conditions prior to the critical approach event correspond to those that are typically experienced in real world driving. More generally, scenarios should have real-world validation.
• Scenarios should emphasize high lead vehicle decelerations. The 0.39-g deceleration levels gave the best results and have been used in previous CAMP surprise trial research.
• Scenarios should emphasize cases where the relative speed differential is high, particularly when the lead vehicle is stationary.
• Scenarios should emphasize last-second hard braking or hard steering over last-second “normal” maneuvers.
• Crash rates should not be used as a metric, and instead, attention should be focused on the interpretation of last-second maneuver onset behavior.
Investigations and analyses of braking behavior of inexperienced drivers in emergency situations have shown that the Brake Assist System which determines an appropriate emergency braking operation and assists in applying the necessary braking force is effective in terms of active safety. While the system designed to detect an emergency braking operation and to consistently apply the maximum braking force is effective for those drivers who cannot apply sufficient braking effort, the system will make experienced drivers feel disagreeable because the system may work against their intention to control the vehicle. Consequently, an ABS actuator was utilized to develop a type of Brake Assist System with excellent control performance and less disagreement. For the covering abstract see IRRD E102514.
Aims:
Myopia may have protective effects against diabetic retinopathy (DR). However, the data from epidemiologic studies are inconsistent. We aimed to examine the association between myopia and DR by conducting a meta-analysis.
Methods:
We identified studies by searching the PubMed and EMBASE databases. Study-specific odds ratios (ORs) were pooled using a fixed or random effects model. Myopic eyes were defined as having a spherical equivalent (SE)<-0.5 diopters (D). Myopic SE, each diopter decrease in SE toward myopia, and each millimeter increase in axial length (AL) were used as independent surrogate variables for myopia.
Results:
Data from 6 population-based and 3 clinic-based studies were included in the analyses. Myopic SE (compared with emmetropic eyes) and each millimeter increase in AL were associated with a decreased risk for DR (pooled odds ratio [OR], 0.80 and 0.79, respectively; 95% confidence interval [CI], 0.67-0.95 and 0.73-0.86, respectively; P=0.011 and 0.000, respectively). Each millimeter increase in AL was also associated with a decreased risk for vision-threatening diabetic retinopathy (VTDR) (pooled OR, 0.70; 95% CI, 0.60-0.82; P=0.000). No significant association between each diopter decrease in SE and DR was observed.
Conclusions:
Our meta-analysis suggests that individuals with myopia exhibit a decreased risk of developing DR or VTDR. An increased AL plays a critical role in this protective effect.
BAS assists driver's by automatically increasing their braking power during an emergency brake event when the driver is unable to apply a sufficient brake force. There are two performance requirements that BAS must fulfill in order to be employed effectively. One is the ability to activate when the driver suddenly applies brakes in an emergency while the other is the ability to provide additional assistance. Further study of BAS activation timing and degree of assistance in relation to driver acceptance is needed. The driver's acceptance of BAS refers to the BAS activation only during an emergency. A study was conducted to clarify drivers' emergency braking characteristics and measure the frequency of BAS activation during normal braking. One aim of the study was to verify driver characteristics during emergency braking on a test course. The study measured the brake pedal speed, force, and stroke during emergency braking along with the driver's compatibility with BAS activation conditions. Another task was to evaluate BAS with a driving simulator (DS). This study measured the frequency of BAS activation during normal braking by varying the BAS activation timing and degree of assistance. It also examined what the effects and side effects of varying these BAS parameters on the driver. In conclusion this study evaluates how varying BAS activation timing and its degree of assistance has on the drivers.
Dynamic Brake Support (DBS) is a safety system that has been applied to various passenger cars and has been shown to be effective at assisting drivers in avoiding or mitigating rear-end collisions. The objective of a DBS system is to ensure that the brake system is applied quickly and at sufficient pressure when a driver responds to a collision imminent situation. DBS is capable of improving braking response due to a passenger car driver's tendency to utilize multi-stage braking.Interest is developing in using DBS on commercial vehicles. In order to evaluate the possible improvement in safety that could be realized through the use of DBS, driver braking behavior must first be analyzed to confirm that improvement is possible and necessary. To determine if this is the case, a study of the response of truck drivers' braking behavior in collision imminent situations is conducted. This paper presents the method of evaluation and results.Data was drawn from a prior NHTSA simulator study and showed that many drivers exhibited multi-stage braking during four different imminent crash scenarios. Results from analysis indicate that a DBS system capable of detecting and intervening at the most conservative level of multi-staging presented could be successful in assisting drivers in avoiding or at minimum mitigating 30% of the collisions presented in this document. Further opportunities for assisting drivers based on reducing brake application time are also shown to exist. These results suggest that a DBS system may be valuable for heavy vehicles. .
Many experimental studies use repeated lead vehicle braking events to study the effects of forward collision warning (FCW) systems. It can, however, be argued that the use of repeated events induce expectancies and anticipatory behaviour that may undermine validity in terms of generalisability to real-world, naturalistic, emergency braking events. The main objective of the present study was to examine to what extent the effect of FCW on response performance is moderated by repeated exposure to a critical lead vehicle braking event. A further objective was to examine if these effects depended on event criticality, here defined as the available time headway when the lead vehicle starts to brake. A critical lead vehicle braking event was implemented in a moving-base simulator. The effects of FCW, repeated event exposure and initial time headway on driver response times and safety margins were examined. The results showed that the effect of FCW depended strongly on both repeated exposure and initial time headway. In particular, no effects of FCW were found for the first exposure, while strong effects occurred when the scenario was repeated. This was interpreted in terms of a switch from closed-loop responses triggered reactively by the situation, towards an open-loop strategy where subjects with FCW responded proactively directly to the warning. It was also found that initial time headway strongly determined response times in closed-loop conditions but not in open-loop conditions. These results raise a number of methodological issues pertaining to the design of experimental studies with the aim of evaluating the effects of active safety systems. In particular, the implementation of scenario exposure and criticality must be carefully considered.
While traffic accident fatalities in Japan have been declining, the number of injuries has continued on an upward trend for many years. One salient aspect of that rising trend is the number of casualties attributed to rear-end collisions. In 2005, such accidents accounted for approximately 35% of all fatalities and injuries. Regarding ordinary passenger cars, many of the drivers of the struck vehicles in rear-end collisions suffer slight neck injuries, while nearly all of the drivers of the striking vehicles are not injured. In this study, the influence of vehicle properties and human attributes on the incidence of neck injuries in rear-end collisions was analyzed using an integrated accident database developed by the Institute for Traffic Accident Research and Data Analysis (ITARDA). The results revealed, among other things, that an active head restraint system, which is one type of anti-whiplash device, is effective in suppressing the occurrence of neck injuries; that females tend to be injured more often than males; that age and generation influence the tendency for men to be injured; and that the trip purpose influences the tendency for neck injuries to occur. This tendency for generation and trip purpose to exert such an influence suggests the possibility that the health consciousness of the parties involved in rear-end collisions might affect the incidence of neck injuries. Among the other issues discussed in this paper is the concern that neck injuries due to rear-end collisions might increase in the future.
The design of active safety systems capable of helping avoiding a crash or reducing the collision severity requires data on how drivers behave in accident situations. These systems must be triggered when drivers actually need assistance. They must enhance insufficient reactions and limit unsuitable ones without being in conflict with drivers' natural behavior. The Laboratory of Accidentology, Biomechanics and human behavior, PSA Peugeot Citroën -Renault (LAB), has conducted experiments on driving simulators and on test tracks to analyze driver's behavior in emergency situations. Two of these experiments concern front-to-rear accident situations, each one involving more than 100 representative common drivers. The first study was carried out on a simulator with different accident scenarios : an adverse vehicle stopped or driving slowly at the top of a hill, a vehicle coming into the driver's lane from a parking area, or a vehicle driving in front of the subject then suddenly braking. The second study was carried out on a test track. The subjects were following a vehicle pulling a trailer that suddenly broke away and strongly braked. In both studies, the drivers' actions on the controls and the vehicle dynamics were recorded along with videos from driver's hands, feet and face and from the scene. The results show the benefit of such studies for the specification of active safety systems. These experiments revealed the inefficiency of braking actions of common drivers in emergency situations. The results provided a basis for the determination of triggering criteria of emergency brake assist and enabled to give recommendations on control strategies. Moreover, these experiments pointed out the benefit of emergency brake assist in terms of collision avoidance rate and crash speed reduction.
Driving is a task that requires the timely detection of critical events and relevant changes in traffic circumstances. Adaptation of speed and safety margins allows drivers to control the time available to react to potential hazards. One of the basic safety margins in driving is the time headway preserved with respect to cars ahead. To avoid rear-end collisions, drivers have to detect decelerations of lead cars. It can be assumed that fast or abrupt decelerations of the lead car are detected faster than slow or gradual decelerations. Moreover, expected decelerations are presumably detected faster than unexpected decelerations. Drivers' responses to rather abrupt and more gradual decelerations of the lead were investigated in a driving simulator. Situational traffic cues were used to manipulate the driver's expectations. Drivers adjusted the timing of their responses very well to the level of deceleration of the lead car. If cues in the environment indicated that the lead car was likely to decelerate, drivers reacted faster. Moreover, drivers increased their headway before the lead car actually started to decelerate, which can be considered an anticipatory response. In general, anticipation allows drivers to maintain their preferred headway and control time pressure in driving.
The minimum total braking time (i.e. the braking reaction time plus the accelerator-to-brake movement time) plays an important role in defining a minimum following gap (MFG). This study was designed to obtain a lower limit for this gap. Total braking times (TBT) of a group of 51 male and female young athletes were monitored during real driving conditions. Sudden braking applied by a leading private passenger vehicle initiated the trials. A within-subject design was used to study the effects of different factors on braking time. Individuals performed a series of semi-counterbalanced trials at two following distances (6 and 12 m), two speeds (60 and 80 km/h) and three expectancy stages (naïve driving, partial knowledge, and full knowledge of the forthcoming manoeuvre). A three-way repeated measures ANOVA showed no major effects of ‘speed’, but major effects of the ‘expectancy’ and the ‘distance’ factors. The experiment yielded a mean TBT of 0·678 s (SD = 0·144 s) for trials averaged over distances and speeds in the naïve condition only. The data emphasize the role played by pre-cues in the braking response prior to emergency stops. Both the level of awareness of the forthcoming manoeuvre and the distance between vehicles appear to determine the response time. The descriptive statistics presented may also provide the basis for an objective, acceptable and legally valid minimum time gap for prosecution of ‘careless’ drivers.
In many countries, motorcyclists are over-represented in traffic collision fatalities and injuries compared to vehicle registrations. Why drivers may violate the right-of-way of motorcyclists traveling as lead vehicles in front of drivers is empirically examined in two studies that were conducted with a moderate-fidelity driving simulator. The purpose of the first study was to determine if drivers, who also held a motorcycle license (N=16), drove cars differently than regular drivers (N=16) around motorcycles. The two groups did not differ on responses to motorcycling braking events, which was consistent with previous research on car following. The second study compared the driving performance of sixteen novice teenage drivers (M=16.2years of age) to 15 experienced drivers (M=32.9) over the span of six monthly simulator sessions. Novice drivers’ perception response times (PRT) to the braking events were significantly longer than those of the experienced drivers. PRTs to motorcycle and lead vehicle braking events decreased over sessions. For all participants, PRTs to the motorcycle events were longer than to the car events. The implications of these results for motorcyclists and drivers with different levels of experience are discussed.
Human perception-brake reaction time (RT) studies have reported a wide variety of results. By analyzing a large number of data sets, however, it is possible to estimate times under specific conditions. The most important variable is driver expectation, which affects RTs by a factor of 2. When fully aware of the time and location of the brake signal, drivers can detect a signal and move the foot from accelerator to brake pedal in about 0.70 to 0.75 sec. Response to unexpected, but common signals, such as a lead car's brake lights, is about 1.25 sec, whereas RTs for surprise events, such as an object suddenly moving into the driver's path, is roughly 1.5 sec. These times are modulated somewhat by other factors, including driver age and gender, cognitive load, and urgency.
The present paper describes a study concerned with the effect of alarm timing on driver trust and behaviour with a Forward Collision Warning System (FCWS). In this driving simulator experiment three different kinds of alarm timing (late/middle/early) were compared with respect to driver braking strategy and driver trust. The results showed that early alarm timing led to a more timely response to an imminent collision than either middle or late timing. With respect to driver trust, trust in late alarm timing is low compared with early or middle alarm timing. Furthermore the relationship between the timing of accelerator release and the timing of alarm onset is an important cause of changes in trust and if alarms are presented after drivers have already started to brake then trust is impaired. Middle alarm timing has the potential to improve driver braking response to imminent collisions. Possible benefits and drawbacks of FCWS are discussed from the viewpoint of alarm timing.
Forward collision warning (FCW) systems can reduce rear-end vehicle collisions. However, if the presentation of warnings is perceived as mistimed, trust in the system is diminished and drivers become less likely to respond appropriately. In this driving simulator investigation, 45 drivers experienced two FCW systems: a non-adaptive and an adaptive FCW that adjusted the timing of its alarms according to each individual driver’s reaction time. Whilst all drivers benefited in terms of improved safety from both FCW systems, non-aggressive drivers (low sensation seeking, long followers) did not display a preference to the adaptive FCW over its non-adaptive equivalent. Furthermore, there was little evidence to suggest that the non-aggressive drivers’ performance differed with either system. Benefits of the adaptive system were demonstrated for aggressive drivers (high sensation seeking, short followers). Even though both systems reduced their likelihood of a crash to a similar extent, the aggressive drivers rated each FCW more poorly than their non-aggressive contemporaries. However, this group, with their greater risk of involvement in rear-end collisions, reported a preference for the adaptive system as they found it less irritating and stress-inducing. Achieving greater acceptance and hence likely use of a real system is fundamental to good quality FCW design.
Warning timing and how drivers with and without forward collision warning (FCW) systems react when distracted at the moment a stationary vehicle is revealed directly ahead were investigated. The study was conducted using the Iowa Driving Simulator (IDS). The IDS was equipped with an FCW system that provided auditory warnings based on two warning criteria. A total of 30 subjects were split across three conditions - a baseline of 10 subjects (no warning display), and two warning conditions (early and late) with 10 subjects each. The two warning conditions differed by the duration of an a priori driver reaction component (1.5 and 1.0 s) in the warning algorithm. Drivers' collision avoidance performance in the two warning conditions was compared with that in the baseline condition. Results indicated that the early warning condition showed significantly shorter accelerator release reaction times, fewer crashes, and less severe crashes than both the baseline condition and the late warning condition. The results indicate that the timing of a warning is important in the design of collision warning systems.
Changes in drivers, vehicles, and roadways pose substantial challenges to the transportation safety community. Crash records and naturalistic driving data are useful for examining the influence of past or existing technology on drivers, and the associations between risk factors and crashes. However, they are limited because causation cannot be established and technology not yet installed in production vehicles cannot be assessed. Driving simulators have become an increasingly widespread tool to understand evolving and novel technologies. The ability to manipulate independent variables in a randomized, controlled setting also provides the added benefit of identifying causal links. This paper introduces a special issue on simulator-based safety studies. The special issue comprises 25 papers that demonstrate the use of driving simulators to address pressing transportation safety problems and includes topics as diverse as neurological dysfunction, work zone design, and driver distraction.
University of Michigan, Ann Arbor, ITS Research Center of Excellence http://deepblue.lib.umich.edu/bitstream/2027.42/1068/2/86649.0001.001.pdf
Perception of the lead car's braking was measured on-road when subjects of various levels of driving experience were looking at a digital display located at the lower part of the windscreen, at the speedometer level, or in the mid-console. The brake lights of the lead car were either working normally or switched off. The results indicated that the detection of the lead car's brake lights, in daylight, is substantially impaired when a following driver is looking at the speedometer area and brake lights do not contribute to detection at all when he/she is looking at a target in the mid-console. Driving experience did not influence performance in detecting a closing headway in peripheral vision, in contrast to improvement in lane-keeping found in an earlier study. It is suggested that such differential ability in using peripheral vision for lane and distance-keeping may mislead experienced drivers when they follow another vehicle and perform certain in-car tasks.
The total braking time (TBT) distribution is used as an input to two important traffic safety parameters: minimum following gap and stopping sight distance. It is therefore important to accurately estimate the TBT distribution. However, the previously published results on TBT distribution vary widely and confuse practitioners. In this paper, a meta-analysis is used in an effort to investigate the sources of variation in the studies of TBT. According to the results of the meta-analysis, significant characteristics of the mean of total braking time are the awareness level of the driver and the country in which the experiment took place. In addition to these two characteristics, both the type of brake stimulus and the distance away from the brake stimulus are found to be influential characteristics on the variance of total braking time. Based on several combinations of these factors, TBT distributions are reconstructed. It is recommended that the percentile estimates of TBT used for the minimum following gap and stopping sight distance need to be adjusted.
Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly--and thereby avoid more collisions--than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0% and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.
In order to improve road safety, automobile manufacturers are now developing Forward Collision Warning Systems (FCWS). However, there has been insufficient consideration of how drivers may respond to FCWS. This driving simulator study focused on alarm timing and its impact on driver response to alarm. The experimental investigation considered driver perception of alarm timings and its influence on trust at three driving speeds (40, 60 and 70 mile/h) and two time headways (1.7 and 2.2 s). The results showed that alarm effectiveness varied in response to driving conditions. Alarm promptness had a greater influence on ratings of trust than improvements in braking performance enabled by the alarm system. Moreover, alarms which were presented after braking actions had been initiated were viewed as late alarms. It is concluded that drivers typically expect alarms to be presented before they initiate braking actions and when this does not happen driver trust in the system is substantially decreased.
Rear-end collisions are often quoted as being a major cause of road traffic accidents. In response to this, a great deal of ergonomics research effort has been directed towards the analysis of brake reaction times. However, the engineering solution has been to develop advanced systems for longitudinal control, which, it is argued, will mitigate the problem of rear-end collisions. So far, though, there have been few empirical studies to determine how brake reaction times will be affected by such vehicle automation. This paper presents a literature review summarizing the current state of knowledge about driver responses in non-automated vehicles. The review covers driver factors, vehicle factors and situational factors. Following the review, some empirical data are presented from a driving simulator experiment assessing brake reaction times of skilled and unskilled drivers under two different levels of automation. When compared to previous data gathered during manual driving, there seems to be a striking increase in reaction times for these automated conditions. Implications for the design and safety of automated vehicle systems are discussed.
Scenario criticality determines the effects of working memory load on brake response time
- Engström
Engström, J., 2010. Scenario criticality determines the effects of working memory load on brake response time. In: Krems, J., Petzoldt, T., Henning, M. (Eds.),
Proceedings of the European Conference on Human Centered Design for Intelligent Transport Systems (HUMANIST), Lyon, France, pp. 25-36.
A study on driver behavior during braking on open road
- M Kassaagi
- G Brissart
- J C Popieul
Kassaagi, M., Brissart, G., Popieul, J.C., 2003. A study on driver behavior during braking on open road. In: 18th International Technical Conference on the
Enhanced Safety of Vehicles (ESV), Nagoya (Japan), May.
Development and Validation of Functional Definitions and Evaluation Procedures for Collision Warning/Avoidance Systems. Publication DOT HS 808 964
- R J Kiefer
- D J Leblanc
- M Palmer
- J Salinger
- R Deering
- M Shulman
Kiefer, R.J., LeBlanc, D.J., Palmer, M., Salinger, J., Deering, R., Shulman, M., 1999. Development and Validation of Functional Definitions and Evaluation
Procedures for Collision Warning/Avoidance Systems. Publication DOT HS 808 964. NHTSA, U.S. DOT.
The accident avoidance potential of the motor vehicle: accident data, vehicle handling and safety standards
- Limpert
Limpert, R., Gamero, F.E., 1974. The accident avoidance potential of the motor vehicle: accident data, vehicle handling and safety standards. Proceedings of
the Third International Congress on Automotive Safety, vol. 11.
NHTSA Light Vehicle Antilock Brake Systems Research Program Task 5.2/5.3: Test Track Examination of Drivers' Collision Avoidance Behavior Using Conventional and Antilock Brakes
- E Mazzae
- F Barickman
- G Forkenbrock
- G Baldwin
Mazzae, E., Barickman, F., Forkenbrock, G., Baldwin, G., 2003. NHTSA Light Vehicle Antilock Brake Systems Research Program Task 5.2/5.3: Test Track
Examination of Drivers' Collision Avoidance Behavior Using Conventional and Antilock Brakes. National Highway Transportation Safety Administration,
Washington, DC.
Estimating Driver Response Times
- J W Muttart
Muttart, J.W., 2005. Estimating Driver Response Times.
Publication DOT HS 812 139. NHTSA
Traffic Safety Facts 2013, 2013. Publication DOT HS 812 139. NHTSA, U.S. DOT.
Shanghai 2020 Driving Scenario Models and Traffic Accident Models Development
- X Wang
- X Chen
- B Deng
Wang, X., Chen, X., Deng, B., 2011. Shanghai 2020 Driving Scenario Models and Traffic Accident Models Development. General Motor, Shanghai, Unpublished
Report.
A study on driver behavior during braking on open road
- Kassaagi
Development of a kinematic-based forward collision warning algorithm using an advanced driving simulator
- Wang