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Development and application of connected vehicle technology test platform based on driving simulator: Case study
Abstract
To ensure safety, it is necessary to test the connected vehicle (CV) technology before application. The goal of this study is to provide a case reference for the testing of the connected vehicle technology. The connected vehicle technology test platform is built based on the driving simulator. Taking fog zone, tunnel zone, and work zone as analysis cases, drivers were invited to participate in driving simulation experiments, related data was collected, and the impact of connected vehicle technology on driving behavior and safety was analyzed. The results of the fog zone imply that drivers have a high degree of compliance with the connected vehicle technology. However, it also increases the visual workload of drivers to a certain extent. The results of the tunnel zone indicate that the connected vehicle technology can enhance driving safety by enabling drivers to remain cautious. The results of the work zone demonstrate that the connected vehicle technology is able to promote drivers’ ability of controlling speed and lane-changing. Overall, the results show that the connected vehicle technology has a positive effect on enhancing driving behavior and safety. The research framework and the development of the connected vehicle technology test platform based on the driving simulator given in the paper are dynamic and reproducible, which provides a reference for researchers in related fields, and the case analysis in this paper enriches the research of connected vehicle technology.
... It remains necessary to explore the combination of HMI and DMS in fog zones to further improve driving behavior and safety. e driving simulator has become the most used test tool for the study of connected vehicle technology because of its real-time data acquisition, controllable events and driving trajectory, and human-machine dual-in-loop capabilities [24]. Driving simulators have proven to be effective tools for connected vehicle technology and driving behavior analysis [25][26][27]. ...
... ey are widely used for studying the effect of connected vehicle technology on driving behavior in fog zones, such as the response behavior of drivers [24] and the impact on vehicle operating eco-characteristics [28]. Moreover, a test platform that can realize real-time data exchange between driving simulators and external terminals through a User Datagram Protocol (UDP) and deliver real-time messages to drivers through the HMI is developed based on the driving simulator [29]. ...
... Referring to the method that determines the required sample size of drivers [24], the number of drivers in this study is more than 32, which meets the sample size requirements. is study analyzes the impact of different levels of connected vehicle technology on driving behavior and safety in fog zones. ...
Driving speeds are higher on freeways, and the visibility reduction occurring in freeway fog zones often increases traffic accidents. This study aims at assessing the impact of different levels of connected vehicle fog warning systems on driving behavior and safety. A connected vehicle fog warning system is developed based on driving simulators, and virtual scenes are developed based on fog zones. The connected vehicle technology includes three levels: a normal level, a level including a human-machine interface, and a level with both a human-machine interface and dynamic message signs. Speed and lateral deviation are chosen as assessment indicators and combined with sample entropy to evaluate the impact of the connected vehicle fog warning system on safety. The deceleration ratio of the warning point is used to evaluate the efficiency of the connected vehicle fog warning system. Results show that the connected vehicle fog warning system can significantly reduce driving speed, and that there are differences in the speed-reduction effectiveness for different technical levels. The connected vehicle fog warning system can reduce the lateral deviation and improve the lateral driving safety. From the perspective of change stability, speed safety entropy and lateral deviation safety entropy are increased, which indicates that the connected vehicle fog warning system will negatively impact safety because of the additional workload. Drivers’ responses are more pronounced in the human-machine interface group compared to the group with dynamic message signs, where the drivers maintained a lower speed. This study provides a reference for the studies on connected vehicle technology based on driving simulators and supports the optimization, design, and evaluation of connected vehicle fog warning systems.
... The age range of the subjects was 18 to 60 years, with a mean age of 30.21 ± 9.08 years. This sample size is adequate to provide robust and reliable answers to the research questions [23]. The placement of wall and ground emergency evacuation signage is the same in schemes 1, 2, 3, and 4, as shown in Figure 3. ...
... The age range of the subjects was 18 to 60 years, with a mean age of 30.21 ± 9.08 years. This sample size is adequate to provide robust and reliable answers to the research questions [23]. ...
The emergency evacuation sign system is crucial for the safety and sustainable development of urban rail transit. Dynamic emergency evacuation signs, which offer real-time guidance during emergencies, are gaining prominence. There is an urgent need to develop a dynamic system that balances perceptual and cognitive visibility. Against this backdrop, this study built a simulation experiment platform based on BIM, Unity, and VR. Eight experimental scenarios were created using the platform: two emergency events (fire/fire accompanied by power outage) × four emergency evacuation signage systems (static emergency signage system/dynamic dissuasion emergency signage system/dynamic dissuasion emergency signage system with flashing/dynamic dissuasion emergency signage system with flashing and auxiliary information), and experimental testing was completed. The evacuation behavior parameters of 39 passengers were extracted and used to construct a multidimensional indicator system. Subsequently, generalized estimation equations were applied to investigate the impact mechanism of signage systems on passengers’ evacuation behavior. Finally, the coupling coordination degree model was used to quantitatively evaluate the coupling coordination level of four emergency evacuation signage systems under different emergencies. Results indicate that compared to static signage system, the three sets of dynamic identification system schemes have a positive impact on passenger evacuation behavior and significantly reduce the number of decision-making errors. Particularly in high-risk scenarios involving fire accompanied by power outages, the dynamic dissuasion emergency signage system with flashing and auxiliary information outperforms others by achieving a better balance of reliability, efficiency, and safety. This study investigates the efficacy of various emergency evacuation signage systems across diverse emergency scenarios, offering insights for the enhanced design of such systems and thereby fostering the sustainable development of urban rail transit infrastructure.
... However, they are not capable of indicating the strength of drivers' responses. To this end, relative speed change is adopted to infer the drivers' perception for the anticipated collision risk and strength of drivers' response (Zhao et al., 2019c;2021b). Also, strength of response is positively correlated to drivers' perception (Zhao et al., 2019c). ...
... Additionally, among the 258 observations, drivers might have misunderstood or ignore the warnings and have unanticipated responses for about 12% of the time (1.6% for Ps ≤ -0.03 and 10.1% for -0.03< Ps ≤ 0) (Zhao et al., 2021b). ...
With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers' responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers' demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human-machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers' responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers' characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers' awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.
... Komunikasi kendaraan-ke-kendaraan (V2V), komunikasi kendaraan-keinfrastruktur (V2I), dan teknologi penggerak otonom secara luas dianggap sebagai kunci yang memungkinkan pengembangan sistem transportasi cerdas generasi mendatang dan sangat diantisipasi untuk membawa solusi baru ke masalah transportasi yang paling memprihatinkan (Zhao et al., 2021). ...
Peningkatan alat transportasi tanpa seimbangnya sarana dan prasarana menuntut inovasi teknologi, meski teknologi sudah diterapkan, namun belum mampu mengatasi dampak perkembangan ekonomi yang meningkatkan jumlah kendaraan. Pengaturan optimal dapat dilakukan melalui sistem berbasis Internet of Things untuk mengurangi kemacetan dan menghemat energi. Manajemen yang baik diperlukan untuk menyeimbangkan semua sarana dan prasarana. Melalui pemantauan kendaraan, dapat ditentukan komposisi kendaraan di jalur yang sama untuk dialihkan atau mengatur waktu traffic light. Teknologi kendaraan terhubung dianggap kunci solusi baru untuk masalah transportasi, memungkinkan komunikasi antar kendaraan dan infrastruktur. Connected vehicle technology memungkinkan kendaraan berkomunikasi menggunakan jaringan nirkabel. Penelitian ini melibatkan pengumpulan data dan model matematika untuk memprediksi arus lalu lintas. Hasil perubahan kondisi eksisting setelah menerapkan skenario penggunaan teknologi Connected Vehicle menunjukkan pengurangan Travel Time dengan hambatan dari sekitar 0.21 jam pada tahun 2018 menjadi perkiraan 0.38 jam pada tahun 2030, memberikan implikasi positif terhadap pengelolaan lalu lintas di masa mendatang. Temuan ini memberikan dasar kuat untuk perbaikan kebijakan dan infrastruktur transportasi yang dapat meningkatkan efisiensi dan kenyamanan perjalanan.
... Measurements of crash avoidance are widely used to study conflicts and near-crash events in specific scenarios, while vehicle performance is assessed using measures such as headways, speed, gaps, time to collision (TTC), and conflict rates. The latter aspect is also known as surrogate measures of safety, and many researchers describe the safety benefits of CVs using these and other measures (e.g., Rahman et al. , 2019Yue et al. 2018;Papadoulis et al. 2019;Ali et al. 2020;Zhao et al. 2021;Ezzati Amini et al. 2023;Huang et al. 2023). ...
Road collisions arise from interactions involving human factors, the environment, and road layout. Driving simulators, widely applied in rear-end collision studies, provide a secure environment to explore human errors, which are not observable through microsimulation tools. These simulators also facilitate the examination of driving behaviour in the presence of connected vehicles. This study aims to identify driver-related factors contributing to rear-end collisions in a driving simulator and to detect potential tailgaters behind a connected vehicle with connected cruise control. Using case-control logistic regression, participants with the potential to be involved in rear-end collisions are considered potential tailgaters, while non-potential participants serve as controls. The results reveal statistically significant factors, such as headway time and maximum brake mean values, in relation to rear-end collisions. Furthermore, employing regression outputs, log relative risk, and survival function, with predefined thresholds effectively identifies potential tailgaters, achieving accuracy rates of over 90% and 97%, respectively.
... In our opinion, they should also have a motion system to deliver the feeling of lateral movements to the test subjects. We could only find driving simulator studies for simple connected vehicles [49], but not for CAVs. Therefore, there is plenty of research potential in the area of driving simulators for CAVs, particularly for lane-free traffic. ...
Connected and automated vehicles (CAVs), with the capability to exchange information with other CAVs and the infrastructure, open up new opportunities in road traffic systems including the recently introduced concept of lane-free traffic (LFT). In an LFT environment, vehicles move without the need for traditional fixed lanes. It is an emerging research area where many forms of LFT strategies are being investigated that can benefit road capacity optimization, thus reducing traffic congestion and increasing traffic efficiency. LFT strategies can also benefit from current research on non-lane based driving behavior observed in some countries. Thus, this paper overviews the available literature on the current non-lane based driving and future LFT. It also studies the taxonomy used in literature to clearly distinguish the two traffic modes. It details the current developments in LFT research and the underlying technologies. From these developments, the paper provides an overview of currently available approaches for realizing LFT. Finally, it discusses the policy considerations needed for the seamless integration of LFT into the current infrastructure once CAVs become mainstream.
Road debris should be removed quickly because it can cause road damage and traffic accidents. An automated road debris removal system—called ROBOS—was developed in South Korea to remove road debris automatically and quickly. It can be mounted and used on vehicles over 3.5 tons. The effects of ROBOS on the traffic flow need to be studied before it can be used on actual roads. In this study, these effects were investigated by performing driving and microscopic traffic simulations. The former can be used to analyze drivers’ reactions to new road equipment and driving behavior when detecting road debris. Driving behavior results that met the research purpose were employed in the microscopic traffic simulation to imitate reality. Finally, the number of conflicts and the conflict rate were compared between manual and ROBOS vehicle road debris removal scenarios to analyze traffic safety. The results showed that the overall traffic safety was low when road debris was present in curved sections, where it was generally difficult for the driver to easily detect it, and in the first lane and third lane, where lane changes were limited. Assuming that a ROBOS vehicle was used in these sections, the number of conflicts and the conflict rate can be significantly reduced. This study evaluated traffic safety after the use of a ROBOS vehicle input and obtained basic information needed for decision-making in future road debris removal work, such as the priority of input when choosing between manual removal and removal by a ROBOS vehicle.
Collision warning systems (CWSs) have been recognized as effective tools in preventing vehicle collisions. Existing systems mainly provide safety warnings based on single-directional approaches, such as rear-end, lateral, and forward collision warnings. Such systems cannot provide omni-directorial enhancements on driver’s perception. Meanwhile, due to the unclear and overlapped activation areas of above single-directional CWSs, multiple kinds of warnings may be triggered mistakenly for a collision. The multi-triggering may confuse drivers about the position of dangerous targets. To this end, this paper develops a spatial-state-based omni-directional collision warning system (S-OCWS), aiming to help drivers identify the specific danger by providing the unique warning. First, the operational domains of rear-end, lateral, and forward collisions are theoretically distinguished. This distinction is attained by a geometric approach with a rigorous mathematical derivation, based on the spatial states and the relative motion states of itself and the target vehicle in real time. Then, a theoretical omni-directional collision warning model is established using time-to-collision (TTC) to clarify activation conditions for different collision warnings. Finally, the effectiveness of the S-OCWS is validated in field tests. Results indicate that the S-OCWS can help drivers quickly and properly respond to the warnings without compromising their control over lateral offsets. In particular, the probability of drivers giving proper responses to FCW doubles when the S-OCWS is on, compared to when the system is off. In addition, the S-OCWS shortens the responses time of nonprofessional drivers, and therefore enhances their safety in driving.
Work zones are crucial for infrastructure maintenance and improvement. However, ongoing projects within work zones sometimes can place workers and drivers in dangerous situations. Despite safety regulations, work zone accidents persist with notable severity and frequency. Previous research has explored work zone accident causation, but it has not provided a comprehensive understanding of the factors that impact work zone safety. This paper addresses this research gap by following a multistep methodology. First, a systematic literature review (SLR) was conducted to identify a comprehensive list of 37 factors impacting work zone safety. Second, social network analysis (SNA) was employed to analyze the connectivity between these identified factors, based on the theoretical, mathematical, and computational approaches found in the literature. Third, the factors were grouped into clusters using the k-means clustering technique, considering the overall discussions present in the literature. Last, association rules were mined from the clustered factors to determine combinations of factors that have not been extensively examined together, thereby identifying gaps and deficiencies in the present body of knowledge. The findings of the SNA indicate that design-related factors have received more extensive attention in the literature, whereas certain driver-and state-related factors have received comparatively less attention. The clustering analysis shows that a significant number of theoretically addressed safety factors in the literature frequently are examined together, which indicates a necessity for additional computational and mathematical investigations to gain a more comprehensive understanding of these factors. Moreover, the association rules highlight the groups of underexamined relationships of factors that affect work zone safety. Ultimately, this research contributes to the body of knowledge by consolidating the studies concerning work zone safety and using them to provide a robust roadmap for future research, thereby enhancing the advancement of this field and ultimately leading to improved work zone safety practices.
In automated driving, takeover behavior is crucial to safe transition of control. Based on this fact, the goal of the present study is to construct the driver's takeover behavior mode and verify its effectiveness. In the takeover process, we define the takeover behavior mode, which is the inherent mode of completing the takeover task, as the behavioral theoretical framework that drivers should follow when taking over. We conducted a driving simulation experiment to analyze driver takeover behavior in the fog zone, where non-driving-related tasks were external variables. First, we constructed the takeover behavior mode (situation awareness - decision and reaction - takeover performance). Second, we analyzed the characteristics of each stage and the correlation between each stage of the takeover behavior mode accordingly. The result shows that drivers' performance in each stage differed significantly under different non-driving-related tasks. For example, their saccade and gaze duration were longer on the entertainment task than on the work task, and the driver prioritized the longitudinal stability after taking control of the automated vehicle. Results show that there is a correlation between different stages of takeover behavior mode. In this study, we propose the takeover behavior mode, aiming to help beginners and researchers in this field understand the driver takeover process more intuitively and provide behavioral theoretical support for subsequent studies. In addition, the takeover behavior mode can provide support for training drivers in real-world driving situations.
The cooperative vehicle-infrastructure system (CVIS) offers opportunities to further enhance the potential of energy saving and emission reduction for eco-driving. However, it is still not clear about the effectiveness of CVIS on promoting eco-driving nor the multivariate effectiveness of CVIS designed for eco-driving on traffic flow and safety characteristics. A dedicated ecological lane (Ecolane) and ecological human-machine-interaction (EcoHMI) based CVIS (Ecolane-HMI-CVIS) were developed based on driving simulator technology. An experiment of 35 participants was conducted to study eco-driving behavior and final utility to identify the influence of Ecolane-HMI-CVIS. The results indicated that the Ecolane-HMI-CVIS enhanced eco-driving behavior and reduced emission, with CO and NOx significantly reduced by 10.72 % and 9.83 % respectively. The Ecolane-HMI-CVIS reduced the headway and promoted vehicle operation stability, ordering, and improved traffic capacity about 10 %. No negative impact of Ecolane-HMI-CVIS was observed on traffic safety. This study developed a test platform based on driving simulator to explore the multivariate influence of Ecolane-HMI-CVIS. In addition to analyzing the overall effect of Ecolane-HMI-CVIS during the whole experimental section, its detailed influence at each key zone and in spatial change process were also analyzed. This research contributed to better understanding of the working mechanism and effectiveness of Ecolane-HMI-CVIS, and provide technical and policy references of CVIS based eco-driving for traffic management departments.
To clarify the effect of the cooperative vehicle infrastructure system (CVIS) application on drivers’ visual performance, a total of 37 drivers were recruited to drive the simulated roadway in a freeway work zone under baseline and cooperative vehicle environments. Drivers’ attention and concentration on the forward roadway, attention distraction, and attention distribution in both scenarios were analyzed. The results indicated that the CVIS application changed drivers’ information-processing mode in the forward roadway as manifested by higher glance frequency and shorter average dwell time. In addition, more off-road distractions were observed in the range of 500 m in front of the work zone, but focusing on human–machine interfaces (HMIs) was not the main cause. In conclusion, the change in the driver’s attention allocation and the diversion was clarified with the proposed visual link diagram. This paper provides a comprehensive approach to visual assessment of CVIS and contributes to the customized design and optimization of future CVIS-HMI.
The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.
The emergence of Intelligent Connected Vehicles (ICVs) is expected to drastically change various fields in the transportation system—especially traffic safety of road users. Therefore, this special issue aims to facilitate a forum for transportation researchers fostering an exchange of research ideas and experience in traffic safety with a specific focus on operations, planning and management of ICVs. The issue contains thirty-six papers from seven different countries. Topics are classified into seven categories: (1) Driving behavior in the ICV environment; (2) Safety evaluation of ICVs; (3) ICV driving/management strategies; (4) New framework for ICV safety analysis; (5) ICV safety for vulnerable road users; (6) Perception towards ICVs; and (7) Security issues relating to ICVs. The papers are concisely introduced in this editorial. All the papers were invited to present at the International Symposium on Accident Analysis & Prevention in 2021 (ISAAP 2021) and the symposium was successfully held. The research conducted in these articles reveal challenges and future directions in the area of ICVs that include further developing novel methodologies and algorithms for collision-free trajectories of ICVs, testing diverse scenarios in complex environments with mixed traffic, and addressing inherent safety risks of specific vulnerable road users (e.g., older road users, bicyclists, riders of micro-mobility vehicles).
Work zone safety is one of the paramount goals of the safety community. Safety in WZs is a particular concern under foggy conditions as they represent an exogenous factor contributing to high variability in driver behavior. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) applications on driver behavior during foggy weather conditions. A work zone (WZ) was simulated using VISSIM in four sequential areas, including the advance warning, transition, activity, and termination area. The effect of drivers’ increased situational awareness under the effect of WZW was calibrated in VISSIM based on the results of a high-fidelity driving simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Time Exposed Time-to-collision (TET), Time-Integrated Time-to-collision (TIT), and Modified Deceleration Rate to Avoid Crash (MDRAC), were employed to quantify the safety performance of CVs under varying CV Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that an increase in CV-MPR enhances the safety performance of the WZ area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR of up to 60% on I-80 would reduce mean speeds and the standard deviation of speed at each of the WZ areas, leading to more speed harmonization and minimizing the crash risk in WZs.
Traffic crashes can be divided into primary and secondary crashes. Secondary crashes occur as a consequence of primary crashes within their spatiotemporal distances. Secondary crashes comprise nearly 20% of all crashes and 18% of fatal crashes, in which they can possibly have a higher crash severity than the primary crash. Interstate-80 in Wyoming is a major rural corridor with a high freight traffic volumes. The Federal Highway Administration selected Wyoming in which to deploy connected vehicle (CV) technology with a focus on commercial truck safety. Distress and rerouting applications were among the suite of CV pilot applications. Very few studies have investigated the safety performance of CVs in mitigating the risk of secondary crashes on heavy trucks, more specifically under adverse weather conditions. This study filled this gap by conducting a driving simulator experiment to assess the effectiveness of CV distress and rerouting applications in mitigating the effects of secondary crashes. A total of 23 truck drivers were recruited to this study. The analysis was conducted on the vehicle kinematics obtained from the driving simulator. A CV and a nonCV scenario were designed to compare the participants’ driving behavior under adverse weather conditions. The results showed that the tested CV applications succeeded in enhancing driving behaviors by reducing the operating speed as well as the speed variation, and all the participants avoided a secondary crash in the CV environment. In addition, the distress notification coupled with the road closure reduced the average operating speed by 26% from the provided speed limit.
Work zone safety is one of the paramount goals of the safety community. The safety concerns in work zones might be exacerbated under foggy conditions, as an exogenous factor, contributing to high driver behavior variability. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) application on driver behavior during foggy weather conditions. A work zone was simulated using VISSIM under four sequential areas, including the advance warning, transition, activity, and termination area. The effect of increased drivers' situational awareness under the impact of WZW was calibrated in VISSIM based on the results of a high-fidelity Driving Simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Modified Deceleration Rate to Avoid Crash (MDRAC), Time Exposed Time-to-collision (TET), and Time-Integrated Time-to-collision (TIT), were employed to quantify the safety performance of CVs under varying CVs Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that the increase in CV-MPR enhances the safety performance of the work zone area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR up to 60% on I-80 would reduce mean speed and standard deviation of speed at each of the work zone areas, leading to more speed harmonization and minimizing crash risk in work zones.
Traffic crashes can be divided into primary and secondary crashes. Secondary Crashes occur as a consequence of primary crashes within their spatiotemporal distances. Secondary crashes comprise nearly 20% of all crashes and 18% of fatal crashes, in which they can possibly have a higher crash severity than the primary crash. Interstate-80 in Wyoming is a major high freight rural corridor. The Federal Highway Administration selected Wyoming to deploy the Connected Vehicle (CV) technology with a focus on commercial truck safety. Distress and rerouting applications were among the suite of CV pilot applications. Very few studies investigated the safety performance of CVs in mitigating the risk of secondary crashes on heavy trucks, more specifically under adverse weather conditions. This study filled this gap by conducting a driving simulator experiment to assess the effectiveness of the CV distress and rerouting applications in mitigating the effect of the secondary crashes. A total of 23 truck drivers were recruited in this study. The Analysis was conducted on the vehicle kinematics obtained from the driving simulator. A CV and a non-CV scenario were designed to compare the participants driving behavior under adverse weather conditions. The results showed that the tested CV applications succeeded in enhancing the driving behaviors by reducing the operating speed as well as the speed variation, and all the participants avoided a secondary crash under the CV environment. Additionally, distress notification coupled with the road closure reduced the average operating speed by 26% from the provided speed limit.
(The YouTube video link: https://www.youtube.com/watch?v=D38yQxXGGQw&feature=youtu.be )
Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings displayed on a human–machine interface (HMI), thus reducing crashes caused by human error. This paper developed a driving simulator test bed to assess the readability and usefulness of the Wyoming CV applications. A total number of 26 professional drivers were recruited to participate in a driving-simulator study. Prior to driving the simulator, the participants were trained on both the concept of CV technology and the developed CV applications as well as the operation of the driving simulator. Three driving simulation scenarios were designed. For each scenario, participants drove two times: one with the HMI turned on and another one with the HMI turned off. After driving the simulator, a comprehensive revealed-preference survey was employed to collect the participants’ perceptions of CV technology and Wyoming CV applications. Results show that the Wyoming CV applications were most favored under poor-visibility driving conditions. Among the Wyoming CV applications, forward collision warning and rerouting applications were experienced as the most useful. Approximately 89% of the participants stated that the Wyoming CV applications provided them with improved road condition information and increased their experienced safety while driving; 65% of the participants stated the CV applications and the HMI did not introduce distraction from the primary task of driving. Finally, this paper concludes that the design of CV HMI needs to balance a trade-off between the readability of the warnings and drivers’ capability to safely recognize and timely respond to the received warnings.
Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.
In this paper, we analyzed driver behavior during automated driving in two experimental conditions, a Driving Simulator (DS) and a Wizard of Oz vehicle (WOz). Twenty-nine drivers in the DS condition and nine drivers in the WOz condition performed three different requests to intervene (RTI) during automated driving (AD). Three variables were measured, the number of control checks during AD and non-driving related tasks (NDRT), the reaction time to resume manual control and the strategy used to recover control. Differences were found concerning road monitoring during NDRT, there are more interruptions in the WOz condition than in the DS condition. Additionally, the strategies used to recover control were different between conditions, the steering wheel and brake pedal were used more often in the WOz condition while the accelerator was used more often in the DS condition. However, no difference was found concerning reaction time to resume control.
A general procedure for the validation of a driving simulation environment for the analysis of gap-acceptance behavior was developed in this study. It allows to test whether a synthetic indicator of gap-acceptance behavior (the mean critical gap) shows significant differences when computed on the basis of field observations versus observations collected in the simulated environment. If such differences are not significant, driver behavior can be considered similar in the two contexts, thus supporting validation of the driving simulation environment. In order to demonstrate its effectiveness, the proposed procedure is applied to the case of a three-leg roundabout located in the Veneto region (Italy). The results show that the mean critical gap estimated in the field and the mean critical gap estimated in the virtual environment are not significantly different. The proposed procedure can be applied in various contexts in which gap-acceptance behavior is a central element in terms of safety and operational performance of the traffic system under analysis.
Improving work zone safety remains a prime challenge for the transportation sector in the United States. In particular, the frequency and severity of work zone crashes involving large trucks in rural freeways are alarming. Lack of compliance with the instructions provided at work zones results in increased crash risk. In-vehicle advanced warning systems enabled by Connected Vehicle (CV) technology have the potential to prompt appropriate driver response, make navigation more predictable, and improve overall work zone safety. This study falls under the umbrella of the WYDOT Connected Vehicle Pilot Program and seeks to investigate the impacts of the Pilot's real-time weather and work zone notifications on the behavior of truck drivers in rural freeway work zone settings under poor visibility. Twenty professional truck drivers participated in this simulator study. The driving scenarios were designed to mimic the driving conditions experienced on Wyoming Interstate 80. Findings suggest that exposure to the CV notifications has promising safety benefits manifested in improved driver behavior and response. Furthermore, both the weather and work zone notifications acquired high approval from the participants in terms of usefulness and ease of understanding. Nonetheless, the display of multiple work zone warnings on the Human Machine Interface may had introduced little to moderate distraction for some participants. Overall, this study brings forth valuable lessons that are being funneled to support informed decision making to enhance the Pilot's existing Human Machine Interface design.
Conflict vehicle movements at intersections are one bottleneck of traffic mobility on urban networks. Recently, with the development of connected vehicles, regulating conflict movements by controlling individual vehicles to improve intersection performance has attracted a lot of research efforts. This study aims at developing an advanced vehicle control system with connected vehicles to reduce vehicle delays at intersections without the help of signals. An intersection delay model is proposed to predict the total vehicle delay at one intersection and to search for the optimal speed control actions to manage traffic under mixed connected and non‐connected vehicle environment. The simulation experiments illustrate that the control system is able to reduce average vehicle delay by up to 95%, and the sensitivity analysis of vehicle demand and market penetrate rates of connected vehicles is also conducted to understand the benefits of the system at different traffic and connected vehicle environments.
This study evaluated the effectiveness and acceptance of four connected vehicle features, i.e. Emergency Electronic Brake Lights (EEBL), Emergency Vehicle Warning (EVW), Roadworks warning (RWW) and Traffic Condition Warning (TCW) which were presented via a mobile phone mounted near the line of sight. A driving simulator study was conducted in which 36 drivers were exposed to different levels of urgent and critical situations. They involved the approach of an emergency vehicle, an emergency braking of a lead vehicle, a roadworks area and a congested section of a road. All these events took place in a simulated motorway scenario. In the EEBL event, the vehicle braking ahead with the brake lights on was either visible or not (between-subjects). Whereas no effect of RWW and TCW were observed on driving behaviour, results showed that drivers who were shown the EEBL warnings had shorter braking and decelerating response times, and a slower mean speed during the events, and this was independent of brake lights visibility. The EVW resulted in participants giving way to the emergency vehicle (i.e. staying on the slow lane instead of overtaking slower vehicles) more frequently than those who did not receive the warning. The mobile phone app was accepted and considered usable. Locating the mobile phone in different locations within the drivers' line of sight (i.e. dashboard, instrument cluster) did not impact significantly neither drivers’ attitudes nor behaviour. Additional in-vehicle information systems could enhance safety and allow emergency vehicles to get faster to their destination.
Variable speed limit systems are used to improve the traffic conditions by adjusting the speed limits based on the current traffic situation. Advances in vehicle technology have made it possible to use connected vehicles in variable speed limit systems. Connected vehicles can continuously transmit information about their speed and location, which can be used to estimate the current traffic conditions at arbitrary locations. In this study, we propose a variable speed limit system based on connected vehicles. The aim is to also allow application of variable speed limits for non-recurrent bottleneck mitigation at arbitrary locations, unlike today's systems which require densely placed detectors or are limited to beforehand known bottleneck locations. The proposed system is evaluated by microscopic traffic simulation. The results indicate that the variable speed limit system manage to improve traffic efficiency in a simulated incident scenario.
Recently, the development of a fully connected transportation network has received special attention from researchers, federal and state government agencies, and public and private stakeholders. The concept of connected vehicles relies on vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communication technologies, which requires a robust platform to allow for not only creativity and interoperability but also the ability to interact with the complex human behavior. Connected vehicles research relies on the usage of test beds to ...
BACKGROUND: In the present study, the epidemiologic aspects of road traffic crashes in South East of Iran are described.METHODS: This cross-sectional study included the profile of 2398 motor vehicle crashes recorded in the police office in one Year in South East of Iran. Data collected included: demographics, the type of crash, type of involved vehicle, location of crash and factors contributing to the crash. Descriptive statistics were used for data analysis.RESULTS: Collisions with other vehicles or objects contributed the highest proportion (62.4%) of motor vehicle crashes. Human factors including careless driving, violating traffic laws, speeding, and sleep deprivation/fatigue were the most important causal factors accounting for 90% of road crashes. Data shows that 41% of drivers were not using a seat belt at the time of crash. One- third of the crashes resulted in injury (25%) or death (5%).CONCLUSIONS: Reckless driving such as speeding and violation of traffic laws are major risk factors for crashes in the South East of Iran. This highlights the need for education along with traffic law enforcement to reduce motor vehicle crashes in future.
The aim of this study is to develop a driving simulator test bed for a connected vehicle environment and study the impact of communicating safety messages on driver behavior. This was conducted by enabling a lead vehicle to communicate alert messages to the simulator when certain time-to-collision thresholds were reached. Thirty participants, grouped into aggressive and non-aggressive drivers, were allowed to drive the simulator twice; once with the alert messages, and another without the alert messages. Using time-to-collision as a performance measure, a t-test for dependent samples showed that for non-aggressive drivers, there were no differences in their driving behavior. However for aggressive drivers, their driving behavior showed a significant improvement in their overall safety. The findings not only lend credence to the safety benefits of the connected vehicles technology, but also means that a driving simulator test bed can be harnessed to achieve similar goals as physical test beds.
This study focused on driving inside road tunnel and is aimed to establish how drivers behave inside tunnel as
well as approaching it and exiting from it. The paper analyses the effects of tunnel on drivers performance using
driving simulator. A tunnel scenario is reproduced in CRISS simulator and driving parameters are compared with the data of a control scenario, characterized by the same road alignment, but without tunnel. Results match
several findings of epidemiological studies on real-world tunnel, providing strong support to the use of driving simulation for studying drivers’ behaviour and its consequence on road safety.
The purpose of this review is to describe the statistical methods available to determine sample size and power analysis in
clinical trials. The information was obtained from standard textbooks and personal experience. Equations are provided for
the calculations and suggestions are made for the use of power tables. It is concluded that sample size calculations and power
analysis can be performed with the information provided and that the validity of clinical investigation would be improved
by greater use of such analyses.
Cet article de revue décrit les méthodes statistiques utilisées au cours des épreuves cliniques pour déterminer la taille
d’un échantillon et l’analyse de sa puissance. L’information provient des manuels standards et de l’expérience de l’auteur.
Des équations sont fournies avec des suggestions sur l’usage des tables de puissance. En conclusion, avec cette information,
il est possible d’effectuer les calculs de la taille d’un échantillon et l’analyse de sa puissance; ces analyses amélioreraient
la validité d’une étude clinique si on les utilisaient plus.
The accidents in the freeway are frequent and more serious on foggy days. Connected vehicle (CV) technology as a new technology can inform drivers of fog conditions in advance so the drivers can adjust their driving behaviors through Human Machine Interface (HMI) and Dynamic Message Sign (DMS) in the fog warning system. The level of driver's compliance with a fog warning system is the key to assessing the effectiveness of the fog warning system. To evaluate the compliance level of fog warning system, the study established a CV system testing platform based on driving simulator, and analyzed the changes in driving behavior and influencing factors for drivers under three different visibility conditions (No fog, light fog and heavy fog) based on the platform, finally, comprehensive evaluation of the optimal fog warning system under different foggy conditions based on the compliance level were made. Research indicators are divided into three aspects: 1) the response degree, including the mean speed, the minimum speed in the fog zone, the difference of the speeds when entering and leaving the fog zone, the proportion of speed following, and the speed reduction proportion prompt; 2) the response start time, which is the time to start slowing down to the speed limit; and 3) the response time duration, namely, the time in which the speed limit is followed. The results show that the response degree of the driver to the fog warning system is high, the fog warning system can effectively reduce the driving speed of the drivers and improve the speed following proportion of the drivers, the deceleration ratio of the driver at each warning point is relatively high, and the influencing factors of various indicators are complex but are mostly related to visibility and technical level. Comprehensive evaluation results show that the warning mode of the combined HMI and DMS has the highest level of compliance under light fog conditions, and when fog concentration increases, compliance level of fog warning system with HMI only is higher than others. The study establishes a reference platform for CV system and provides methods and index system for the compliance level of CV research.
Lane-changing manoeuvre is one of the risky manoeuvres performed by drivers either to reach the planned destination (i.e., mandatory lane-changing; MLC) or to achieve better driving conditions (i.e., discretionary lane-changing; DLC). Essentially both lane-changing types require the driver to acquire surrounding traffic information for efficient and safe lane-changing decisions. However, this does not discount the fact that both these lane-changings are fundamentally different from each other as the urgency of lane-changing is much higher during MLC compared to DLC. Real-time driving aids in a connected environment promise to assist during the lane-changing decision-making process, but the differential effectiveness (or usefulness) of real-time driving aids in a connected environment for these two lane-changing types remains unexplored due to the novelty of a connected environment and the consequent scarcity of data. To fill this research gap, this study collected lane-changing data from 78 participants who performed MLC and DLC in the CARRS-Q Advanced Driving Simulator. Participants were asked to drive in three randomised driving conditions: baseline condition (without driving aids), real-time driving aids in a connected environment with perfect communication, and delayed driving aids in a connected environment. While surrogate measures of safety are analysed and compared using descriptive statistics, a hybrid framework of data mining and classical statistical modelling is employed to examine the usefulness of the real-time driving aids in a connected environment for two lane-changing types. We find that the crash risk associated with MLC is significantly reduced in the driving conditions providing driving aids compared to that of DLC. Results also reveal that the probability of engaging in a hard-braking event decreases for both the lane-changing types in the presence of real-time driving aids in the connected environment driving conditions, but a higher decrease in magnitude is found for MLC. Age and gender-related differential impact have been observed where young and male drivers have a higher possibility of engaging in a hard-braking event when driving without driving aids, but the presence of real-time driving aids reduces such risk. This study concludes that the usefulness (or effectiveness) of the real-time driving aids in a connected environment is a function of the urgency of a task, which is evidently higher during MLC, thus providing the maximum advantage during MLC.
Praise for the Second Edition:"This is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics. "This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered " - Journal of the Royal Statistical Society. Sample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation. Features: Compares the relative merits and disadvantages of statistical methods for sample size calculations. Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages. Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and women's health. Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches. This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.
This paper employed a high-fidelity driving simulator to investigate the impacts of the Wyoming Department of Transportation (WYDOT) Connected Vehicle (CV) Pilot’s Traveler Information Messages (TIMs) on drivers’ speed selection and the safety benefits of their speed harmonization. Three driving simulator experiment scenarios were developed to simulate the typical traffic and weather conditions on the rural Interstate 80 (I-80) in Wyoming. A total of 25 professional drivers from the WYDOT and trucking industry were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the effects of CV TIMs on their speed selection. The results showed that average speed profiles under CV scenarios were generally lower than under baseline scenarios, particularly for winter conditions (snowy and severe weather). The variance of speed under CV scenarios was found to be significantly lower than the baseline scenarios, indicating that CV TIMs have the potential to harmonize the variations in speed. In addition, for the work zone driving simulator experiment, this research revealed that the mean time-to-collision (TTC) under baseline scenario is approximately 40 % lower than CV scenario, and the mean deceleration to avoid a crash (DRAC) under baseline scenario is approximately 19.3 % higher than CV scenario. These findings suggest that CV TIMs can reduce the risk of crashes. Research findings would provide the WYDOT with early insights into the effectiveness of CV TIMs, which could assist with developing more efficient transportation management strategies under adverse weather conditions.
Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings, commonly displayed on a human–machine interface (HMI), thus reducing the likelihood of crashes caused by human error. Nevertheless, the presence of an in-vehicle CV HMI may pose an increasing threat to driver distraction, particularly for truck drivers and under high workload driving conditions. With this concern, this research investigated the effects of a HMI developed by the Wyoming Department of Transportation CV Pilot on truck drivers’ cognitive distraction and driving behavior through a driving simulator experiment. Revealed preference survey and vehicle dynamics data were employed to assess the cognitive distractions of the Pilot’s HMI. Simulation results indicated that when CV warnings were displayed on the HMI, they did not introduce significant effects on participants’ longitudinal and lateral control of the vehicle. Nevertheless, from the revealed preference survey, it was found that approximately 27% of the participants indicated that the CV HMI tended to introduce additional visual workload for them, particularly when approaching an active freeway work zone under reduced visibility condition. In this regard, this research pointed out that the design of CV warnings and HMI displays needs to incorporate drivers’ ability to recognize and react safely to the received CV warnings to minimize the cognitive distractions introduced by the CV HMI.
The connected environment provides surrounding traffic information to drivers via different driving aids that are expected to improve driving behavior and assist in avoiding safety-critical events. These driving aids include speed advisory, car-following assistance, lane-changing support, and advanced information about possible unseen hazards, among many others. While various studies have attempted to examine the effectiveness of different driving aids discretely, it is still vague how drivers perform when they are exposed to a connected environment with vehicle-to-vehicle and vehicle-to-infrastructure communication capabilities. As such, the objective of this study is to examine the effects of the connected environment on driving behavior and safety. To achieve this aim, an innovative driving simulator experiment was designed to mimic a connected environment using the CARRS-Q Advanced Driving Simulator. Two types of driving aids were disseminated in the connected environment: continuous and event-based information. Seventy-eight participants with diverse backgrounds drove the simulator in four driving conditions: baseline (without driving aids), perfect communication (uninterrupted supply of driving aids), communication delay (driving aids are delayed), and communication loss (intermittent loss of driving aids). Various key driving behavior indicators were analyzed and compared across various routine driving tasks such as car-following, lane-changing, interactions with traffic lights, and giving way to pedestrians at pedestrian crossings. Results suggest that drivers in the perfect communication scenario maintain a longer time-to-collision during car-following, a longer time-to-collision to pedestrian, a lower deceleration to avoid a crash during lane-changing, and a lower propensity of yellow light running. Overall, drivers in the connected environment are found to make informed (thus better) decisions towards safe driving.
In response to developing and/or diminishing foggy conditions, the variable speed limit application in a connected vehicle environment (CV-VSL) can estimate and deliver recommended travel speeds to individual drivers, which can help to reduce crashes when visibility conditions change. This study aims to quantify the effectiveness of the CV-VSL application by exploring drivers’ reactions to warnings (e.g., recommended travel speeds). In order to analyze the effectiveness of the CV-VSL application, a connected vehicle testing platform was established based on a driving simulator, and characteristics of the drivers’ speed adjustments after receiving warnings were analyzed with respect to different levels of visibility (i.e., no fog, slight fog, and heavy fog). This study also examined the effect of warnings on drivers in different impact zones (i.e., clear zone, transition zone, and fog zone). Three indicators were identified: 1) speed at the end of the clear zone, 2) maximum deceleration rate in the transition zone, and 3) average speed reduction in the fog zone. Throughout the experiment, the relationship between speed adjustments and the level of visibility was explored. The results indicated that the CV-VSL application is effective in making drivers reduce travel speeds in all three types of zones. Furthermore, it appeared that the CV-VSL application could help manage travel speeds prior to vehicles entering the transition zone, and influence drivers’ braking decisions upon encountering reduced visibility. It was also found that the CV-VSL application was more effective in heavy fog conditions than in light fog conditions. The connected vehicle testing platform based on the driving simulator provided a new method for evaluating the effectiveness of in-vehicle messaging generated by connected vehicle applications.
In 2015, the U.S. Department of Transportation (U.S. DOT) selected Wyoming as one of three sites to develop, test, and deploy a suite of connected vehicle (CV) applications on a 402-mi Interstate 80 corridor. One of the Wyoming’s key CV applications is the variable speed limit (VSL) warning, which aimed to provide commercial truck drivers with real-time regulatory and advisory speed limits to help in better managing speeds under adverse weather conditions, and reducing potential speed variances that may cause traffic collisions. This paper developed a driving simulator testbed to assess the impact of the Wyoming’s CV-based VSL (CV-VSL) application on truck drivers’ behavior under adverse weather conditions. A total of 18 professional truck drivers were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the impact of the CV-VSL warnings on their driving behavior. Simulation results showed that when the advisory speed limits were lower than 55 mph, participants generally followed the VSLs displayed on the CV human–machine interface (HMI). In addition, traffic flows utilizing CV-VSL technology tend to exhibit lower average speeds and speed variances compared with baseline scenarios. These effects of CV-VSL warnings can bring potential safety benefits, as reduction in average speeds and speed variances are effective surrogate measures of safety, that is, lower risk of crashes, under adverse weather conditions.
With emerging new technologies, the vehicles in the future with connected vehicle systems (CVS) will be equipped with the ability to communicate with each other and aim to provide drivers with information in a timely and reliable way to improve driver safety. This study was designed to investigate the interaction effects of warning lead time (2.5 s vs. 4.5 s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was designed to simulate a connected vehicle environment with missed warnings due to failures in the communication network of the CVS. With regard to the response types, the results showed that notification warnings led to a lower probability of braking response and a higher probability of braking and steering response compared with command warnings. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5 s, whereas no significant difference of collision rates was found between two warning styles when the warning lead time is 4.5 s. These results suggest notification warnings should be selected when the warning lead time is longer and the warning systems are highly reliable, which resulted in higher safety benefits and higher subjective rating. Command warnings could be selected when the warning lead time is shorter since they led to more safety benefits, but such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.
Introduction:
Driver distraction has become a significant problem in transportation safety. As more portable wireless devices and driver assistance and entertainment systems become available to drivers, the sources of distraction are increasing.
Method:
Based on the results of different studies in the literature review, this paper categorizes different distraction enablers into six subcategories according to their fundamental characteristics and how they would affect a driver's likelihood of engaging in non-driving related activities. The review also discusses the characteristics and influence of external and internal distractions. The objective of this study is to examine the effect of different distraction sources in fatal crashes with the consideration of a driver's age and sex. Tukey test, chi-square test of independence, Nemenyi post-hoc test, and Marascuilo procedure have been used to investigate the top distraction sources, the trend of distraction-affected fatal crashes, the effect of different distractions on drives in different age groups, and their influence on female and male drivers.
Results:
It was found that inner cognitive inferences accounted for the greatest proportion of driver engagement in distractions. Young drivers show a larger probability of being distracted by in-vehicle technology-related devices/objects. Within the group of young drivers, female drivers showed a higher probability than their male counterparts of engaging in distracted driving caused by in-vehicle technology-related devices. Among six subcategories of distractions, drivers older than 80 years old were found to be most likely affected by inner cognitive interferences.
In the connected vehicle environment, real-time vehicle-state data can be obtained through vehicle-to-infrastructure communication, and the prediction accuracy of urban traffic conditions can significantly increase. This study uses the C++/Qt programming language and framework to build a simulation platform. A two-way six-lane intersection is set up on the simulation platform. In addition, two speed guidance algorithms based on optimizing the travel time of a single vehicle or multiple vehicles are proposed. The goal of optimization is to minimize the travel time, with common indicators such as average delay of vehicles, average number of stops, and average stop time chosen as indexes of traffic efficiency. When the traffic flow is not saturated, compared with the case of no speed guidance, single-vehicle speed guidance can improve the traffic efficiency by 20%, whereas multi-vehicle speed guidance can improve the traffic efficiency by 50%. When the traffic flow is saturated, the speed guidance algorithms show outstanding performance. The effect of speed guidance gradually enhances with increasing penetration rate, and the most obvious gains are obtained when the penetration rate increases from 10% to 40%. Thus, this study has shown that speed guidance in the connected vehicle environment can significantly improve the traffic efficiency of intersections, and the multi-vehicle speed guidance strategy is more effective than the single-vehicle speed guidance strategy.
Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Test (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases in the C-ITS pilot at the city of Ipswich in Brisbane, Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and breaking. The analysis results show that, we can detect the change in speed but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.
Although traffic signals are installed to reduce the overall number of collisions at intersections, certain types, in particular, rear-end collisions are increasing due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposes and evaluates an Advanced Stop Assist System (ASAS) at signalized intersections by using Vehicle-to-Infrastructure (V2I) communication. The proposed system utilizes communication data, received from roadside equipment, to provide approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking at a yellow or red signal. A simulation test bed was modeled using VISSIM, a microscopic simulation software, to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that a penetration rate of at least 60% is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. © National Academy of Sciences: Transportation Research Board 2018.
Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.
One of the principal facets of age-related decline–diminished perceptual ability, can also be viewed as a prominent factor when crossing intersections, particularly rural intersections that have disproportionately high fatality rate and where vehicles travel at higher velocities. Providing information through in-vehicle technology may aid drivers in improving crossing of such intersections. The current study examines the efficacy of an in-vehicle intersection crossing assist system in a real-world rural setting across age groups. Thirty-two, older and younger drivers completed several crossings of a busy rural intersection. Drivers completed two blocks of trials in which the presence/absence of the in-vehicle system was counterbalanced. The results showed a limited impact of the system on driving performance, exhibited in longer wait time before crossing and rising trend towards reduced probability of accepting small crossing gaps. Older drivers performed similarly to younger, although they showed a greater tendency towards conservative driving behaviour. The current study represents an initial effort to examine an in-vehicle intersection crossing assist system in a real-world rural environment, generating results that reveal a potential for these types of systems to be assistive to drivers across age groups and increase the safety at rural intersections.
This paper develops and addresses the implementation issues associated with the field application of an Eco-Speed Control (ESC) system that computes and recommends a fuel-efficient trajectory to drivers using signal timing and phasing data received from downstream-signalized intersections. From an algorithmic standpoint, the proposed process addresses the possible scenarios that a driver may encounter while approaching a signalized intersection. Alternatively, from an implementation standpoint the paper overcomes the challenges associated with communication latency, data errors, real-time computation, and smoothness of the drive in developing the system. The Virginia Smart Road connected vehicle controlled facility at the Virginia Tech Transportation Institute (VTTI) was used to conduct a preliminary proof-of-concept testing of the proposed ESC system. The testing included driving on downhill and uphill approaches for four red indication offset values. In total 192 trips were conducted using four different participants. The analyzed data indicate that the proposed system is very promising, producing an average reduction in fuel consumption levels and travel times in the range of 17.4 and 8.4 percent, respectively.
Driver distraction is a contributing factor to many crashes, and a real-time distraction warning system has the potential to mitigate or circumvent many of these crashes. The objective of this chapter is to describe in detail the distraction detection algorithm AttenD and explain the theory underlying different design choices. Future aspects and distraction warning strategies are discussed also. In summary, AttenD is an eye-tracker-based distraction detection algorithm which identifies visual distraction in real time based on single long glances as well as repetitive glances. The core idea of the algorithm is a two-second time buffer which is decremented when the driver looks away from the road and incremented when the driver looks back at the road. If the buffer runs empty, the driver's state is classified as distracted.
Driver inattentiveness is one of the critical factors that contribute to vehicle crashes. The intervehicle safety warning information system (ISWS) is a technology to enhance driver attentiveness by providing warning messages about upcoming hazards under the connected vehicle environments. A novel feature of the proposed ISWS is its capability to detect hazardous driving events, which are defined as moving hazards with a high potential to cause crashes. The study presented in this paper evaluated the potential effectiveness of the ISWS to reduce crashes and to mitigate traffic congestion. The study included a field experiment that documented actual vehicle maneuvering patterns of accelerations and lane changes, which were used to enhance the realism of simulation evaluations. Probe vehicles equipped with customized onboard units, which consisted of a GPS device, accelerometer, and gyro sensor, were used. A microscopic simulator, VISSIM, was used to simulate a driver's responsive behavior after warning messages were delivered. A surrogate safety assessment model was used to derive surrogate safety measures to evaluate the effectiveness of ISWS in terms of traffic safety. The results showed a reduced number of rear-end conflicts when the ISWS's market penetration rate (MPR) and the congestion level of the traffic conditions increased. The reduced number of rear-end conflicts was approximately 84.3%, with a 100% MPR under Level of Service D traffic conditions. Analysis of the standard deviation of speed showed that a reduction of 39.9% was achieved. The outcomes of this study could be valuable to derive smarter operational strategies for ISWS.
This article estimates the safety potential of a current commercially available connected vehicle technology in real-world crashes.
Data from the Centre for Automotive Safety Research's at-scene in-depth crash investigations in South Australia were used to simulate the circumstances of real-world crashes. A total of 89 crashes were selected for inclusion in the study. The crashes were selected as representative of the most prevalent crash types for injury or fatal crashes and had potential to be mitigated by connected vehicle technology. The trajectory, speeds, braking, and impact configuration of the selected in-depth cases were replicated in a software package and converted to a file format allowing "replay" of the scenario in real time as input to 2 Cohda Wireless MK2 onboard units. The Cohda Wireless onboard units are a mature connected vehicle technology that has been used in both the German simTD field trial and the U.S. Department of Transport's Safety Pilot project and have been tuned for low false alarm rates when used in the real world. The crash replay was achieved by replacing each of the onboard unit Global Positioning System (GPS) inputs with the simulated data of each of the involved vehicles. The time at which the Cohda Wireless threat detection software issued an elevated warning was used to calculate a new impact speed using 3 different reaction scenarios and 2 levels of braking.
It was found that between 37 and 86% of the simulated crashes could be avoided, with highest percentage due a fully autonomous system braking at 0.7 g. The same system also reduced the impact speed relative to the actual crash in all cases. Even when a human reaction time of 1.2 s and moderate braking of 0.4 g was assumed, the impact speed was reduced in 78% of the crashes. Crash types that proved difficult for the threat detection engine were head-on crashes where the approach angle was low and right turn-opposite crashes.
These results indicate that connected vehicle technology can be greatly beneficial in real-world crash scenarios and that this benefit would be maximized by having the vehicle intervene autonomously with heavy braking. The crash types that proved difficult for the connected vehicle technology could be better addressed if controller area network (CAN) information is available, such as steering wheel angle, so that driver intent can be inferred sooner. More accurate positioning in the real world (e.g., combining satellite positioning and accelerometer data) would allow the technology to be more effective for near-collinear head-on and rear-end crashes, because the low approach angles that are common in such crashes are currently ignored in order to minimize false alarms due to positioning uncertainty.
Immediate user self-evacuation is crucial in case of fire in road tunnels. This study investigated the effects of information with or without additional virtual reality (VR) behavioural training on self-evacuation during a simulated emergency situation in a road tunnel. Forty-three participants were randomly assigned to three groups with accumulating preventive training: The control group only filled in questionnaires, the informed group additionally read an information brochure on tunnel safety, and the VR training group received an additional behavioural training in a VR tunnel scenario. One week later, during the test session, all participants conducted a drive through a real road tunnel in which they were confronted with a collision of two vehicles and intense smoke. The informed and the behaviourally trained participants evacuated themselves more reliably from the tunnel than participants of the control group. Trained participants showed better and faster behavioural responses than informed only participants. Interestingly, the few participants in the control group who reacted adequately to the scenario were all female. A 1 year follow-up online questionnaire showed a decrease of safety knowledge, but still the trained group had somewhat more safety relevant knowledge than the two other groups. Information and especially VR behavioural training both seem promising to foster adequate self-evacuation during crisis situations in tunnels, although long term beneficial behavioural effects have to be demonstrated. Measures aiming to improve users’ behaviour should take individual difference such as gender into account.
Driving in fog is a potentially dangerous activity that has been investigated in a number of different ways; however, most have focused on identifying the underlying perceptual changes that result in an inability to perceive speed of vehicle motion. Although the previous research has identified the perceptual changes associated with driving in fog and shows that people are highly likely to perceive their speed to be higher than it actually is, these research studies have not investigated driving behavior when drivers are allowed to maintain speed as they feel appropriate and make use of the vehicle's speedometer. In addition, much of the existing research focuses on speed perception and presents a limited view of other driving performance metrics in terms of lane keeping and event detection. The current study addresses these issues utilizing a driving simulator-based method where fog is simulated as a distance dependent contrast reduction while having participants drive at speeds they feel are appropriate. A number of different instructions and speed feedback mechanisms were tested in order to determine how drivers react when driving in varying levels of fog. Results also include lane keeping measures in order to assess whether drivers are willing to drive at speeds where their lane keeping performance is degraded due to the reduced visibility. Results indicate that, in general, drivers do not tend to slow down significantly until visibility distance is drastically reduced by fog; however, lane keeping ability is maintained throughout most of the range of visibility distances. Lane keeping ability was reduced only when fog results in visibility distances <30 m. Overall, the current study shows that drivers are willing and able to maintain vehicular control at high speed when driving in fog; however, it is important to note that drivers chose to drive at speeds where they would be incapable of stopping to avoid obstacles in the roadway even if they were to identify and react to the obstacle immediately at the border of visibility distance. This research suggests that safety benefits may be gained by convincing drivers to slow down more than they would on their own when driving in fog or enhancing a vehicle's ability to identify and react to hazards that are not visible to the driver. In order to further understand the effects of driving in fog, future naturalistic driving research should focus on identifying and mitigating risky behaviors associated with driving in foggy conditions.
Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.
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