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Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment
Abstract
Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).
... In contrast, Abdulsattar et al. [15,16] highlighted the transformative potential of CAVs in altering traffic flow dynamics, particularly in RW zone scenarios. Liu et al. [17] tested a connected vehicle algorithm that could increase the throughput and decrease delay, but it was designed for ramp merging and not for sudden lane drops in RW zones. ...
... The integration of C-ITS presents an unprecedented opportunity to optimise traffic performance by enabling cooperative control of lane change manoeuvres, not only for the target vehicle but also for the surrounding vehicles, thus significantly improving safety and efficiency on the roads. C-ITS could obtain wide-area traffic information and perform high-precision control, using on-board sensors and AVs control functionality [50,51] [17] also analysed lane-changing behaviours in environments with multiple lanes and CAVs. Additionally, Greenblatt and Shaheen [55] found that successful lane changes in congested traffic are challenging for individual vehicles without cooperation from surrounding vehicles. ...
The mobility and safety benefits of Connected and Automated vehicles (CAVs) are an ongoing area of research. While several studies demonstrate the advantages of CAVs, there remains a gap in quantifying these benefits over conventional Automated Vehicles (AVs). Roadworks (RWs) are particularly challenging driving environment for AVs/CAVs. This paper evaluates the safety and mobility benefits of CAVs over AVs in RW conditions. The primary focus is on examining the lane change behaviour of AVs/CAVs at different design speeds, specifically 40km/h, 60km/h, and 80km/h. A simulated environment is developed to integrate CAVs and RW areas featuring lane closure. To achieve this, state-of-the-art models (lateral behaviour: Minimising Overall Braking Induced by Lane Changes (MOBIL); and longitudinal behaviour: Intelligent Driver Model (IDM)) were utilised, incorporating improvements to simulate near misses and crashes incidents. Furthermore, the study delves into the crucial aspect of safety by investigating the effects of model parameters on the chances of traffic crashes. The research findings demonstrate that CAVs start lane change manoeuvre early, particularly under high traffic flow conditions. Specifically, at a speed of 40 km/h and under high traffic conditions, CAVs demonstrated a success rate ranging from 91% to 96%, while AVs exhibited a substantially lower success rate, ranging only between 10% and 11%. The simulation results further highlight a lack of collisions in AVs/CAVs during lane change at RW. This research serves as a valuable contribution to the field of automated driving, providing insights into the optimisation of lane change behaviours and safety considerations at RW environments.
... Although the driver compliance was measured based on the driver's response, and it was roughly divided into two levels, i.e., low compliance and high compliance. On the contrary, Liu et al. [21] used a driver compliance index which was a random integer ranging between 0 and 100. In addition, Cui et al. [22] incorporated the compliance rate of CV driver to theoretically analyze the effects of CVs on the stability of homogeneous and heterogeneous traffic flows. ...
... Hence, the steady-state conditions of heterogeneous traffic flow mixing CVs and RVs are obtained, i.e., Eq. (21), shown at the bottom of the page. ...
Connected vehicles (CVs) are conductive to promoting the transition from purely regular vehicles to purely connected autonomous vehicles where CVs are regarded as regular vehicles equipped with driver assistance systems (DASs). CVs can share status information (i.e., position, velocity, etc.) between each other through vehicle-to-vehicle communication technology, and DASs can provide CV drivers with motion suggestions (e.g., optimal velocity, etc.) based on the shared information. However, CV drivers may not completely follow these suggestions, and may combine them with their own driving experience and perception of traffic information which may be influenced by the interference of vehicles on the adjacent lane. Hence, this paper proposes a two-lane car-following model to simulate CVs under connected environment. The proposed model incorporates the compliance rate of CV drivers to DASs and considers the interference of vehicles on the adjacent lane to CV drivers by introducing the visual angle and its change rate of CV drivers. Linear stability analysis and numerical simulations of homogeneous and heterogeneous traffic flow are performed. Results show that the increases in the penetration rate of CVs and the compliance rate of CV drivers promote traffic stability, while the interference of vehicles on the adjacent lane reduces traffic stability.
... (1) First, to evaluate the human aspects that affect the safety of CVs, such as driver compliance [20,21] . Sharma et al. [21] thoroughly investigated the the effect of driver compliance on the mixed traffic environment of CVs and traditional vehicles including both high-compliance and low-compliance drivers. ...
... (1) First, to evaluate the human aspects that affect the safety of CVs, such as driver compliance [20,21] . Sharma et al. [21] thoroughly investigated the the effect of driver compliance on the mixed traffic environment of CVs and traditional vehicles including both high-compliance and low-compliance drivers. ...
Connected vehicle (CV) is regarded as a typical feature of the future road transportation system. One core benefit of promoting CV is to improve traffic safety, and to achieve that, accurate driving risk assessment under Vehicle-to-Vehicle (V2V) communications is critical. There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones: (1) the CV environment provides high-resolution and multi-dimensional data, e.g., vehicle trajectory data, (2) Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment, e.g., the multiple interacting objects and the time-series variability. Hence, this study proposes a driving risk assessment framework under the CV environment. Specifically, first, a set of time-series top views was proposed to describe the CV environment data, expressing the detailed information on the vehicles surrounding the subject vehicle. Then, a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment. It is proved that this model can reach an AUC of 0.997, outperforming the existing machine learning algorithms. This study contributes to the improvement of driving risk assessment under the CV environment.
... In another study, Abdulsattar et al. (2018), Abdulsattar et al. (2019) evaluated the travel time and safety performance of connected vehicles in work zone and the results revealed that the impact of connected vehicles is significant under any traffic demand when compliance rate is up to 10%. Liu et al. (2017) introduced and tested a connected vehicles algorithm considering the operational performance of the system. Although, the tested algorithm could significantly increase the throughput and decrease the delay when the compliance rate is medium or high, the nature of ramp merging is different due to the fact that in a work zone there is a sudden drop in the number of lanes, while ramps are designated facilities for accelerating and merging. ...
... However, V2V and V2I communications are explored in highway operations and safety applications. Studies have been performed to evaluate CV's impact on highway operations (Maitipe, 2011;Várhelyi et al., 2015;Davis, 2016;Genders & Razavi, 2016;Liu et al., 2017). CV application in work zones by locations of vehicle congestion and travel times was studied (Maitipe, 2011). ...
Typical traffic control and operations at work zones are not effective in mitigating the work zone bottleneck, as the common early merge behavior limits utilizing the available capacity in the closed lane. Recently, using late merge strategy is encouraged to fully utilize the closed lane, therefore increasing efficiency of the work zone. In practice, the late merge strategy does not work effectively due to the lack of compliance by drivers. This paper is aimed at exploring two work zone late merge strategies with and without enabling connected vehicles (CV) technology. This first strategy is the late merge without CV enabled. The second strategy incorporates CV-enabled cooperative merge to enhance the work zone efficiency through communications between vehicles in the open and closed lanes. The CV-enabled strategy employs a rule based decentralized control algorithm. The paper has implemented both late merge strategies in microsimulation and evaluated the operational performances. Three major performance measures, which are throughput, delay and queue length, are used in the evaluation. The results indicate that the operational performance of the CV-enabled cooperative late merge is superior to the late merge strategy without CV in most cases, especially when traffic demand is moderate, while both late merge strategies outperform the traditional early merge strategy. Moreover, effects of the control variables including the traffic demand, heavy vehicle percentage, lane distribution percentage, late merge length, reduction of gap acceptance factor, and compliance rate, have been analyzed. Ultimately, sensitivity analysis has been performed to investigate the trend of effects from the significant variables control.
... CVDS-IDM explicitly incorporates driver compliance behaviour, which is modelled using Prospect theory (Kahneman and Tversky, 1979). Liu et al. (2017) did propose a methodology that incorporated the ADAS-affected driving behaviour modelling (driver compliance, perception reaction time, and ADAS influence). Driver compliance as per Liu et al. (2017) was modelled as a random number whereas in the case of CVDS-IDM the driver compliance modelling was based on theoretically, behaviourally, and practically sound prospect theory. ...
... Liu et al. (2017) did propose a methodology that incorporated the ADAS-affected driving behaviour modelling (driver compliance, perception reaction time, and ADAS influence). Driver compliance as per Liu et al. (2017) was modelled as a random number whereas in the case of CVDS-IDM the driver compliance modelling was based on theoretically, behaviourally, and practically sound prospect theory. To comprehensively investigate the impact of driver compliance on the mixed traffic, two classes of drivers based on compliance levels are considered, namely the high-compliance drivers and the low-compliance drivers. ...
In the foreseeable future, connected vehicles (CVs) will coexist with traditional vehicles (TVs) resulting in a complex mixed traffic environment and the success of CVs will depend on the characteristics of this mixed traffic. Therefore, before the large scale deployment of CVs, it is important to examine how CVs will influence the characteristics of the resultant mixed traffic. To achieve this aim, this study models the mixed traffic of TVs and CVs, and examines the traffic flow disturbance, efficiency, and safety. Intelligent Driver Model (IDM) with estimation errors is utilised to model TVs since it incorporates human factors such as estimation errors. Whereas, connected vehicle driving strategy integrated with IDM is utilised to model CVs because it incorporates driver compliance, a critical human factor for the success of CVs. Moreover, two classes of drivers based on their compliance levels are considered, namely the high-compliance drivers and the low-compliance drivers, to comprehensively investigate the impact of driver compliance on the mixed traffic of CVs and TVs. Two simulation experiments are performed in this study. The first experiment is used to measure traffic flow disturbance and safety while the second is used to measure the traffic flow efficiency. Furthermore, a total of 7 mixed traffic environments are generated in each experiment via different combinations of TVs, CVs with low compliance drivers, and CVs with high compliance drivers. Another important point considered in the simulation is the spatially distribution of CVs in the platoon. As such, three platoon policies are investigated. In the first policy i.e., the best case, the CVs are spatially arranged with a motive to maximise benefits from CVs whereas in the second policy i.e., the worst case, the CVs are spatially arranged with a motive to minimise benefits from CVs. Finally, in the third platoon policy i.e., the random case, the CVs are distributed randomly in the platoon. This study demonstrates the importance of the spatial arrangement of CVs in a platoon at a given penetration rate and its impact on traffic flow disturbance, efficiency, and safety. Moreover, findings from this study underscores that CVs can supress the traffic flow disturbance, and enhance traffic flow efficiency, and safety; however, traffic engineers and policy makers have to be cautious regarding how CVs are distributed in a traffic stream when deploying these vehicles in the real world traffic environment.
... The resulting insights can lead to targeted interventions such as road design modification, speed control, and safe driving promotion. Additionally, microscopic vehicle trajectory data can aid the development of connected and autonomous vehicle environments by providing detailed insights into the movement patterns and behaviors of vehicles, which can be used to improve vehicle control systems [3], optimize traffic flow [4], and real-time evaluation of traffic safety [5]. Specifically, Benjamin Coifman [6] believes that microscopic vehicle trajectories are intended to help advance traffic flow theory in general and car-following models in particular [7]. ...
Roadside LiDAR systems can generate real-time microscopic vehicle trajectories applicable to develop intelligent transportation systems and aid the operations of connected and autonomous vehicles. Tracking is the most crucial data processing step to generate accurate and reliable trajectories of road users from raw point clouds collected from LiDAR sensors. In this paper, a new tracking mechanism is proposed for real-time tracking, which is based on the 2D LiDAR data structure with the Simple Online and Real-Time Tracking (SORT) algorithm. The traditional method of using bounding boxes to identify vehicles is replaced by center points of vehicles inspired by track-by-point approach. The developed index that integrates the distance between the LiDAR serves as a more accurate way of defining the spatial location of vehicles. The proposed methodology was evaluated using data collected by a 32-channel portable LiDAR at three signalized intersections. The results showed that the proposed method has a higher tracking accuracy and a faster computation speed compared to the traditional bounding box approach, indicating improvement toward real-time applications. Index Terms—Real-time tracking; Roadside LiDAR; Trajectory; Track by point; SORT.
... Nevertheless, the recent evidence has demonstrated that, even when these features may be already integrated to their vehicles, only a limited percentage of drivers might be properly informed about the functioning, usefulness and actual potential of ADAS for road safety [7], [8]. In this regard, some studies [9]- [14] have identified several latent barriers for ADAS and other automated features, that may be limiting their potentiality and functionality among drivers [15], including: a relative disinterest from drivers for ADAS intervention during risky driving [9], the interference of ADAS in the skill development of novice drivers [9]- [11], the often low driver' trust on assisting features [12], [13] and the potential lack of proper understanding and using of ADAS in particular age-based groups, such as elderly drivers [7], [14]. Another evident barrier for the interaction between users and technicalcommercial information on ADAS is the (sometimes inconsistent) nomination of these technologies between sources, that may explain potential confusions among customers at the moment of interacting with potentially useful information in this regard; as an example, the Automatic Emergency Braking (AEB) may be frequently presented using terms such as ''active braking'', ''front automatic braking'', ''pre-collision assist'', etc., making difficult its identification through a single nominal standard [16], [17]. ...
Advanced Driver Assistance Systems (ADAS), created for enhancing the driving experience and actively preventing road crashes, have been progressively incorporated in vehicle designing essentially during the last decade. However, the literature has shown how some of these assisting technologies are not used by drivers in tandem with their potential. The aims of this study were, first, to examine the availability and demand of ADAS technologies among Spanish drivers and, secondly, to explore the perceived constraints and discouraging reasons for avoiding the use of ADAS available in their vehicles. For this national cross-sectional study, data from 1,207 Spanish drivers were analyzed. The results of this study show that, on one hand, GPS navigation, rain sensors and automatic lighting are the most frequently used ADAS features in Spain and, on the other, that gestural control, E-call and post-collision emergency braking are the less demanded. Also, there are age and gender-based differences in the valuation of certain ADAS features. Further, low perceived value, lack of confidence and potential distractibility constitute the main constraints perceived by drivers to actively use these assisting technologies while driving. In this regard, and jointly with a progressive vehicle automatization, a deeper emphasis on driver training, safety and efficiency-related benefits of ADAS technologies may strengthen its acceptance and progressive inclusion in everyday driving. INDEX TERMS Advanced driver assistance systems (ADAS), drivers, demand, reliability, disuse.
... Both CVs and CAVs utilize V2V, I2V, and V2I communication through Dedicated Short-Range Communication (DSRC) technology (Abboud et al., 2016). Previous research primarily concentrated on using CV to improve compliance rates (Ghanadbashi et al., 2024a), but did not fully address merging strategies or changes in gap acceptance and cooperation, which are key benefits of CV technology (Algomaiah and Li, 2022;Liu et al., 2017). Moreover, the automated driving feature can facilitate cooperation between vehicles in the open and closed lanes from a greater distance using DSRC technology. ...
A work zone is a section of road with closed lanes for maintenance, forcing vehicles to merge and creating congestion bottlenecks on the highway. Emergency Vehicles (EVs) are vital for incident response, with response times closely tied to fatality rates. EVs often face challenges when navigating work zones, and despite their importance, little attention has been given to improving their movement through these areas, highlighting the need for a system that enables quicker EV passage in work zones. While traffic management strategies are implemented in work zones, their effectiveness for EVs remains unexplored. This leaves a gap in understanding work zone management for EVs, ensuring their fast and safe passage. This paper proposes the ADAPtive Emergency MERGing (ADAPT-EMERG) algorithm to address this gap. This algorithm controls vehicles longitudinally for smooth merging into the open lane. It integrates merging approaches, headway adjustment between vehicles, and Variable Speed Limit (VSL) rules to set speed limits. Simulation results show that the ADAPT-EMERG algorithm reduces average travel times by 40%, minimises time loss for EVs by an average of 5%, and achieves a throughput increase across various traffic scenarios compared to the state-of-the-art strategies.
... These systems provide real-time feedback to drivers, encouraging smoother acceleration, deceleration, and optimal speed, consequently reducing environmental footprint (Fleming et al., 2018). ADAS systems such as adaptive cruise control (ACC) and traffic sign recognition contribute to optimising traffic flow, thereby reducing stop-and-go traffic patterns and idling, which are major contributors to emissions (Liu et al., 2017).In recent research, a novel approach was presented for the automatic generation of predictive models aimed at forecasting the power behaviour of GPU-based (Graphic Processing Units) edge data centres during runtime, particularly for real-time data analytics applications (Perez et al., 2019). The study utilised real traces of traffic demand and a real CNN-based (Convolutional Neural Networks) application profile on actual GPU devices to validate the effectiveness of the proposed method. ...
... However, these methods often struggle with the inherent rarity of safety-critical trajectories. Liu et al. [10] emphasized the need for fine-tuning optimization under specific accident-prone conditions, but the lack of diverse safety-critical data limits the universality of AV planning solutions. Moreover, while traditional optimization techniques provide valuable insights for enhancing AVs' planning, they often fail to adapt to the complexities of near-collision situations. ...
Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories.
... To ensure computational efficiency, the time horizon T p is set to 70s. Additionally, we investigate the sensitivity of trajectory feasibility to the minimum time headway τ = τ p , varying it from 1.4s to 1.8s, in accordance with safety limits and time headway thresholds [45]. In total, we conduct 20 scenarios (4 × 5) to comprehensively evaluate the performance of the CTSA algorithm. ...
This paper presents a hierarchical tactical merging optimization (HTMO) approach for connected and automated vehicles (CAV) at freeway merging segments. The proposed approach comprises two layers: a merging sequencing layer and a trajectory optimization layer, which are coupled by a hierarchical model that utilizes sequence set and vehicle state variables. In the merging sequencing layer, a sequence set variable is introduced to simplify the sequence space and identify the optimal merging sequence using a customized tabu search algorithm. In the trajectory planning layer, we formulate a two-point-boundary optimal control model for CAV trajectory planning, incorporating a platoon formation strategy to further enhance travel efficiency. To handle outliers caused by variations in the preceding vehicle’s trajectory, we have developed a heuristic trajectory optimization algorithm to ensure the generation of feasible trajectories with predetermined optimal acceleration values as proposed. Numerical experiments conducted demonstrate the robust convergence performance and computational efficiency of the HTMO approach across different arrival flow scenarios and parameters setting, thanks to its utilization of a rolling horizon strategy. Additionally, when combined with the platoon formation strategy, the schedule produced by HTMO significantly reduces total travel time and delay, as evidenced by our findings.
... For example, Forward Collision Warnings (FCW) in ADAS continuously compare the safety indicator, such as Timeto-Collision (TTC) (Hayward, 1972), or brake reaction time of the subject vehicle with the threshold, which is often conservative for stability and safety purposes (Sun et al., 2019). Although some studies attempted to fine-tune the thresholds in ADAS algorithms (Liu et al., 2017) or introduce new RDRA concepts (Mullakkal-Babu et al., 2020), which are still determined by kinematic approaches, neglecting drivers' objective attributes. In addition, current ADAS mainly focuses on car-following and lane-maintaining situations, while it is not sensitive to complex cut-in scenarios. ...
Real-time Driving Risk Assessment (RDRA) is a critical component of traffic safety and is influenced by confounding impacts from drivers, surrounding vehicles, and roadway conditions. Previous studies simplified the RDRA based on kinematic characteristics. However, drivers perceived the risks not only through kinematic characteristics but also by anticipating the behaviors of surrounding participants and execute collision-preventive maneuvers. In this study, an innovative RDRA framework based on the Psycho-Physical Field (PPF) is proposed. Specifically, the PPF of the subjective vehicle exerts repulsive forces on intrusive risk sources in different directions, of which the two-dimensional field distribution is determined by physical collision-related measures and regulated by the psychological characteristics of behavior anticipations and risk perception abilities. The proposed method was first analyzed through theoretical feasibility analyses and verified for over 450 high-risk events from naturalistic driving data, including three typical types of scenarios: car-following, lane-changing, and being cut-in/off. Moreover, the adaptability was further validated through three cases and compared with the risk warning functions of Mobileye. The results showed that the proposed method can provide accurate risk evaluations, and identify potential hazards about 2 s in advance for high-risk cut-in events.
... There are a few other studies in the literature that try to optimize ACC settings to maximize traffic flow. For example, Liu et al. studied the optimization of ACC system parameters, however, this study is based on an offline optimization technique that does not cope well with dynamically changing traffic conditions (6). Other studies address this limitation by adjusting the ACC system settings adaptively in real time according to dynamic traffic conditions (e.g., Elmorshedy et al. [5], Kesting et al. [7], and Spiliopoulou et al. [8,9]). ...
Adaptive cruise control (ACC) systems are increasingly offered in new vehicles in the market today, and they form a core building block for future full autonomous driving. ACC systems allow vehicles to maintain a desired headway to a leading vehicle automatically. Recent research demonstrates that (1) shorter headways lead to higher throughput, and (2) the effective use of ACC can improve traffic flow by adapting the desired time headway in response to changing traffic conditions. In this paper we show that, although shorter headways result in higher capacity, flow breakdown still occurs if traffic densities at bottlenecks are allowed to exceed the critical density. Therefore, dynamic traffic control near bottlenecks is still necessary to avoid bottleneck activation and capacity loss. We propose an adaptive reinforcement learning (RL) headway controller that uses ACC headways to optimize traffic flow and minimize delay. Based on state measurements, the controller dynamically assigns an optimal headway value for each freeway section within a control cycle. In a freeway simulation example, we first demonstrate that different nondynamic headway assignment strategies failed to avoid congestion and traffic breakdown. We then present a dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts the desired headway according to the changing traffic conditions on both the freeway and the ramp to effectively maximize traffic flow and minimize system delay. We quantitatively demonstrate that our DRL dynamic headway control strategy improved traffic and reduced system delay by up to 57% compared with the examined nondynamic headways.
... Due to the complexity and the amount of spatio-temporal data, the computation time spent on processing the data can be prohibitive for real-time applications. A number of emerging solutions use sophisticated techniques that are only computationally feasible for optimization in limited scopes such as a single road segment or a single intersection [9,10]. There is a lack of computationally efficient solutions for improving network-level road safety. ...
With the increasing connectedness of vehicles, real-time spatio-temporal data can be collected from citywide road networks. Innovative data management solutions can process the collected data for the purpose of reducing travel time. However, a majority of the existing solutions have missed the opportunity to better manage the collected data for improving road safety at the network level. We propose an efficient data management framework that uses network-level data to improve road safety for citywide applications. Our framework uses a graph-based data structure to maintain real-time network-level traffic data. Based on the graph, the framework uses a novel technique to generate driving instructions for individual vehicles. By following the instructions, inter-vehicular spacing can be increased, leading to an improvement of road safety. Experimental results show that our framework improves road safety, measured based on the time to collision between vehicles, from the state-of-the-art traffic data management solutions by a large margin while achieving lower travel times compared with the solutions. The framework is also readily deployable for large-scale real-time applications due to its low computation costs.
... Since the start of the 21 st century, there has been a steady increase in the development and implementation of automated driving system (ADS) technology, which followed the uptake of advanced driver assistance systems (ADAS) (Lindgren and Chen 2006;Ziebinski et al. 2017). There are many claims that ADS has the potential to change the way we drive for the better, by improving traffic flow (Hartmann et al. 2017;Liu et al. 2017;Ziebinski et al. 2017), improving traffic safety (Farah and Ziebinski et al. 2017) and general driving comfort and pleasure (Hasenjäger and Wersing 2017;Holzinger et al. 2020), as well as fuel efficiency (Tunnell et al. 2018). And while this may indeed eventually be the case, the actual situation in the coming decades will be much more nuanced due to low levels of CAVs on roads in mixed traffic and teething problems related to the broadening introduction of CAVs (Sohrabi et al. 2021). ...
Traffic safety is arguably the most important traffic metric from a human perspective. Still, many millions of people are killed on roads every year. In this paper, the concept of Herd Immunity for Traffic Safety (HITS) is presented for the first time. This concept focuses on identifying and describing the increased level of safety that is achieved when Connected Automated Vehicles (CAV) and Human Driven Vehicles (HDV) co-exist in mixed traffic. The underlying mechanism is described with a key component being the ability of CAVs to absorb human error and reduce exposure to risk. With increasing levels of CAV penetration, so-called tipping points occur in which the traffic safety grows in proportion to the penetration rate, which is demonstrated by the non-linearity of the penetration–risk relationship. This is demonstrated in theory and experimental cases while requirements to understand and apply the concept more extensively in the future are presented. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
... ACC control strategies can be implemented to reduce the deteriorations caused by adopting conservative system settings as well as increase the improvements by exploiting the full capabilities of such systems. For example, the authors in [33] study the optimization of ACC system parameters, however, this study is based on an off-line optimization technique which does not cope well with the dynamically changing traffic conditions. There are a few studies that address this limitation by adjusting the ACC system settings adaptively in real time according to dynamic traffic conditions such as in [27], [29], and [34]. ...
The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.
... Emerging technologies such as Connected and Automated Vehicles (CAV) require 2 real-time crash-risk estimation to safely maneuver through their surroundings (Liu et al., 2017, 3 Wu et al., 2018. Infrastructure-based intelligent transport system (ITS) technologies such as 4 adaptive traffic signal control (Essa and Sayed, 2020, Jin et al., 2021) require real-time safety-5 related information to enable joint optimization of traffic signal timing for both safety and delay 6 minimization. ...
Traffic conflict techniques are a viable alternative to crash-based safety assessments and are particularly well suited to evaluating emerging technologies such as connected and automated vehicles for which crash data are sparsely available. Recently, the use of multiple traffic conflict indicators has become common in methodological studies, yet it is often difficult to determine which conflict indicators are appropriate given the application context, and the net benefit, in terms of improved crash prediction accuracy, of considering additional conflict indicators. Addressing these concerns, this study investigates the potential benefits of multiple conflict indicators for conflict-based crash estimation models by using a multivariate extreme value modeling framework (with Gumbel-Hougaard copulas) to estimate crash frequency by severity. The selected conflict indicators include Modified Time-To-Collision (MTTC), Deceleration Rate to Avoid a Collision (DRAC), Proportion of Stopping Distance (PSD) and expected post-collision change in velocity (Delta-V). The proposed framework was applied to estimate the total, severe (Maximum Abbreviated Injury Scale ≥ 3; MAIS3+), and non-severe (MAIS < 3) rear-end crash frequencies at three four-legged signalized intersections in Brisbane, Australia. Rear-end traffic conflicts were extracted from video data using state-of-the-art Computer Vision analytics. Results show that the prediction performance improvements are not necessarily proportional to the number of conflict indicators used in extreme value models. MTTC and DRAC, combined with the severity indicator Delta-V, were the most suitable predictors of rear-end crashes at signalized intersections. Results suggest that instead of adding more and more conflict indicators, careful selection of compatible conflict indicators (considering their functional differences and empirical correlations) is the best way to enhance the predictive performance of conflict-based models.
... Furthermore, vehicles with such ACC are not always energy efficient, and they may cause shock waves or congestion due to the presence of disturbances in dense traffic. The offline multi-objective optimization technique was used to optimize parameters in order to have a suitable trade-off between safety and performance objectives [25]. These ACCs are unable to finetune their driving behavior dynamically by considering the changing trends of the traffic ahead; therefore, their performances are affected greatly in transient conditions. ...
This paper presents Adaptive Cruise Control (ACC) with look-ahead anticipation, based on the model of ACC used in recent commercial vehicles, to take early decisions in driving a vehicle on the freeway. The existing ACC found in the high-end cars has limited operating range as it often fails to respond smoothly in advance behind a decelerating vehicle. Although advanced techniques, such as model predictive control (MPC), can significantly improve a vehicle’s driving performance, they are associated with high computational complexity and have limited scopes for practical implementation. The proposed look-ahead anticipatory scheme of ACC predicts the relative states of the preceding vehicle using a conditional persistence prediction technique in an adaptive short horizon. With negligible computation cost, it determines the control input using parametric functions prudently for improving driving performance. The proposed scheme is evaluated on multiple vehicles in typical traffic scenarios to examine individual driving behavior and the stability of a vehicle string. Finally, we investigate the influences of a small part of vehicles with the proposed ACC on overall traffic using the AIMSUN traffic simulator and compare performances of overall traffic.
... bottleneck locations relying on feedback control.Liu et al. (2017) proposed an optimized algorithm for 138 connected vehicle environment, which improved the traffic throughput. They adoptedHidas (2005) to 139 incorporate the cooperation as a result of the Advanced Driver Assistance Systems (ADAS). Ali et al. 140 (2019) proposed a lane-changing model for connected vehicles based on game theory method. A ...
This paper explores the efficiency of a novel merging system based on a cooperative late merge strategy (CLMS) to mitigate the capacity reduction in work zones due to lane closure. Cooperative late merge strategies in connected vehicles (CV) and connected and autonomous vehicles (CAV) environments are formulated to enhance throughput by reducing gaps and increasing the synchronized speed in the work zone. We propose decentralized and centralized systems based on vehicle-to-vehicle and vehicle-to-infrastructure communication. The decentralized CLMS incorporates a modified lane-changing model to reflect the cooperative feature under the CV environment. The centralized CLMS is developed to further optimize the work zone throughput based on gap reduction and speed harmonization features
enabled by CAV. The results prove that the decentralized CLMS outperforms other systems by increasing throughput as well as reducing delay and queue length. The centralized CLMS demonstrated substantial improvements compared to other systems. The simulation results prove that the decentralized CLMS improves capacity by 17% and the centralized CLMS by 45%, when compared to a traditional work zone system.
... In this work, the authors aim to investigate the possibility to employ the model-based strategies to control the non-linear time-dependent system, i.e., the full vehicle model with temperature and wear sensitive tyres operating in completely different environmental conditions. To perform the study, the standardized DLC maneuver, currently employed for the validation of virtual driver and advanced driving assistance systems (ADAS) [16,19,20], is implemented in Matlab/Simulink virtual environment. The vehicle and tyre models have been characterized and validated for a reference GT vehicle, identifying the requisite complex tyre-road coupled phenomena concerning the temperature, wear, and road pavement dependencies [21,22]. ...
In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safetycritical situations, where the environmental conditions (rain, snow, ice), the road singularities (oil stains, puddles, holes), and the tyre thermal and ageing phenomena can deeply affect the adherence potential. The objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm in a hardware-in-the-loop real-time environment and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. The multidisciplinary proposal will provide a paradigm shift in the development of strategies and a solid breakthrough towards enhanced development of the driving automatization systems, unleashing the potential of physical modeling to the next level of vehicle control, able to exploit and to take into account the multi-physical tyre variations.
... Aunque esta pregunta pueda parecer trivial -o su respuesta bastante simple-, las pocas evidencias empíricas que han estudiado el impacto objetivo de los sistemas avanzados de ayuda a la conducción en la reducción de accidentes sugieren que, aparte de la necesidad de nuevos desarrollos tecnológicos, la interacción con el usuario es una asignatura pendiente para incrementar el potencial de la automatización del vehículo (incluyendo los ADAS) como elemento de seguridad vial . Un aspecto especialmente interesante de estos estudios es la recolección de evidencias que demuestran que los sistemas de seguridad activa basados en ADAS (por ej., frenada automática de emergencia y control de crucero adaptativo) resultan de amplia eficacia para reducir sustancialmente el riesgo de accidente (Bareiss et al., 2019;Edwards, nathanson & Wisch, 2018;Li et al., 2017), sobre todo si se comparan con los denominados ADAS "informativos" (que alertan del riesgo), dado que la efectividad de estos últimos -aunque también imprescindibles-dependen en gran medida de las capacidades y destrezas del conductor (Hubele & Kennedy, 2018;Braitman, McCartt, Zuby & Singer, 2010). ...
The objective of this report is to investigate and know the status of the
art on scientific studies and research published in the international literature as well as European documents and directives that are related to ADAS systems and their relationship with road safety.
La automatización de los vehículos constituye, en la actualidad,
uno de los temas de mayor relevancia para la movilidad, la seguridad
vial y el desarrollo sostenible de los países, y ofrece diferentes
beneficios para el conductor y para el resto de usuarios de las
vías, así como para la eficiencia y seguridad del tráfico
El objetivo de este informe es investigar y conocer el estado del
arte sobre estudios e investigaciones científicas publicados en la
literatura internacional, así como documentos y directivas europeas
que se encuentran relacionados con los sistemas ADAS y su relación
con la seguridad vial.
... In [6], it was indicated that nearly 18% of the total number of traffic accidents were caused by improper lane changing. Using a prediction model in the Advanced Driver Assistance Systems (ADASs) [7][8][9] could reduce the risk of accidents. erefore, a model for accurate prediction of a driver lane-changing behavior using multiple data fusion is needed. ...
A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.
... Kato et al. (2002) verified that cooperative lane change can improve road capacity and reduce lane change time. Wei et al. analyzed the lane-changing behaviors in the multiple-lane and CAV operational environment (Liu et al., 2017;Tao et al., 2005). Even studies (Ward et al., 2017) have shown that it is difficult for a single vehicle to change lanes successfully in case of traffic congestion without cooperation with its surrounding vehicles. ...
Lane change maneuver is one of the critical resources of traffic oscillation which will jeopardize traffic efficiency and safety. Emergent connected and autonomous vehicle (CAV) technology provides the opportunity to cooperate with CAV movements and conducts cooperative lane change. However, the state of the art indicates that most studies only cooperatively control the host vehicle and the vehicle on the target lane but ignore the traffic flow on the subject lane. Motivated by this research gap, a centralized two-stage optimization-based cooperative lane change (CTO-CLC) approach is introduced for a pure CAV flow in a two lanes highway. The lane change process is studied independently from the aspect of lateral and longitudinal movement. A sine function curve is used for the lateral movement and the cooperative longitudinal movement is divided into two stages according to the physical lane position, i.e. lane change process in original lane (LC-O) and lane change process in target lane (LC-T) stages. The control input and terminal tracking states are considered in the objective function to ensure both the traffic flow smoothness and the tracking performance. The effectiveness of the proposed approach is validated through numerical experiments in MATLAB. The results indicate the cooperative lane change model is available and practical, which can be well carried out in multiple vehicles with a simple real lane change scenario.
... The vehicle width is 1.8 m. The boundary of safe time-headway was given as [1.2 s, 2.4 s] (Liu et al. 2017), and we set T hd = 1.8 s. Each MV was assumed to travel at the speed of 25 m/s, and the RV traveled at the speed of 15 m/s with the acceleration of 1.2 m/s 2 . ...
Weaving sections may cause massive congestion and accident problems. Connected and automated vehicles (CAVs) are acknowledged to improve traffic safety and efficiency through effective communication and control. To this end, this study proposes a centralized cooperative vehicle trajectory planning framework for SAE Level 4 or 5 automation. Specifically, focusing on the complex movements at weaving sections, the longitudinal optimal trajectory control is proposed to avoid collisions. This improves traffic efficiency and reduces fuel consumption and driver discomfort. A sideswipe collision prediction algorithm takes into account the geometric features of vehicles and predicts the time of the collision. The merging sequences model with safety constraints is developed to avoid the collision between the on-ramp and off-ramp vehicles. The effectiveness of the proposed model is validated through simulations, where the proposed method is compared with the baseline to demonstrate its potential in improving safety and reducing the fuel consumption and travel time.
... The transmission of microlevel road weather data by vehicles through V2X communication has been demonstrated in the European WiSafeCar project [91], which is anticipated to benefit the efficiency of road weather management [92]. V2X applications for roadway safety and vehicle safety Abboud et al. [30]; FHWA [64] Intersection collision warning, emergency vehicle pre-emption, work zone alerts, curve speed warning, railroad crossing violation warning Barbaresso and Johnson [65] In-vehicle signage, oversize vehicle warning, red-light or stop-sign violation warning, reduced speed zone warning/lane closure, restricted lane warning, spot weather impact warning Iteris [66] Cooperative collision avoidance Chowdhury et al. [67]; Wang and Li [68] Blind spot warning and lane change warning Theriot et al. [69]; Howe et al. [70] ADAS functionalities Liu et al. [71] Pedestrian detection and warning He and Zeng [72] Deer crossing road detection: with multiple roadside LiDAR (Light Detection and Ranging) sensors deployed to enable real-time, microlevel and high-resolution sensing of road users Emergency vehicle priority Head [79] Adaptive signal control Yao et al. [80] Cooperative adaptive cruise control Huang et al. [81] Dynamic routing support Genders and Razavi [82] Smart DMS, routing support and data-driven apps for freight carriers, transit vehicles and emergency responders Iteris [66]; Akin et al. [83] Road weather V2X applications for motorist advisories and warnings, information for maintenance and fleet management systems, and MDSS Barbaresso and Johnson [65]; Young et al. [84]; FHWA [64] Agency Data V2I applications for probe-based traffic monitoring, probe-based pavement condition monitoring, and performance measures Barbaresso and Johnson [65]; FHWA [9]; Li et al. [85,86] Other V2X applications for passenger infotainment (via in-vehicle Internet access) and car manufacturer services (e.g., point-of-interest notification and remote vehicle diagnostics). ...
Currently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety and mobility challenges on highways enduring snowy winter weather. To address this pressing issue, this commentary proposes that the physical and digital infrastructures should be upgraded to take advantage of emerging technologies and facilitate the vehicle-infrastructure integration (VII), to better inform decision-makers at various levels. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. This commentary envisages the deployment of vehicle-to-everything (V2X) technologies to bring about transformative changes and substantial benefits in terms of enhanced winter safety and mobility on highways. At the center of the commentary is a conceptualized design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to connected infrastructure (CI) applications, to leverage the immensely expanded data availability fueled by better spatial and temporal coverage. The commentary also advances the idea that CI solutions can augment the sensing capabilities and confidence level of connected or autonomous vehicles. The application scenarios of VII system is then briefly explored, followed by some discussion of the paradigm shift towards V2X applications and a look to the future including some identified research needs in the arena of CI. This work aims to inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, because the specific challenges call for systematic, holistic, and multidisciplinary approaches accompanied by concerted efforts in the research, development, pilot testing, and deployment of CI technologies.
... Road traffic injuries are currently the leading cause of death for children and young adults aged 5-29 years, and the number of deaths on the world's roads remains unacceptably high, with an estimated 1.35 million people dying each year [1]. Continuous efforts have been made to improve vehicle safety and mitigate the road traffic injuries [2][3][4][5][6]. With the advent of vehicle active safety technologies, researchers and engineers have become increasingly interested in preventing accidents by Advance Driver Assistance Systems (ADAS) [7,8]. ...
The objective of this study was to explore the relevance between Autonomous Emergency Braking (AEB) control strategy and occupant pre-impact kinematics among a typical real-world cut-in impact scenario. First, the accident scenario was built with PreScan software after accident analysis. Second, a MADYMO simulation model with Active Human Model (AHM) was built and validated with the volunteer test carried out by our team. Finally, the AEB module and related control strategies were introduced into the main vehicle, and the effects of different strategies on the occupant kinematic were evaluated. The simulation results indicated that it was efficient to evaluate the occupant kinematics during pre-impact phase through vehicle and occupant integrated simulation method. The main vehicle’s velocity could be reduced between 5 km/h and 14 km/h respectively after introducing different AEB control strategies, which was less than the one manoeuvred by driver (22 km/h). Earlier activation of the AEB and heavier braking could result in larger up-body displacement, but less final impact velocity, and the maximum head displacement reached 172.56 mm due to the AEB control. Comparing partial braking with detection angle 9° case with 100% braking with detection angle 18° case, the head, thorax and shoulder displacements were increased by 94.8%, 104.1%, and 48.7%. This research is beneficial for the subsequent integrated safety analysis.
... e entry time and the number of vehicles on main road and ramp road are random. e value of the timeheadway can be in the range of [1.2 s, 2.4 s] in different scenarios [30] and here we choose T h � 1. e simulation of the position, velocity, and acceleration is shown in Figures 6-8. e gaming period ends at the moment when the first vehicle reaches the Adjusting Area, and in this simulation, the moment is at 3.12 s. ...
Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.
... These systems have already been introduced in many vehicles and efforts need to be made to increase accuracy for improving driver safety. In addition, the threshold of many conventional ADAS such as systems using perceptual-based warning algorithms are decided as a single value calculated considering the vehicle dynamics (Liu, H. et al., 2017). The kinematic-based warning algorithms have a variable threshold timing for the collision warning, but these algorithms also must assume the driver's reaction time and ego vehicle acceleration. ...
In recent years, many Advanced Driver Assistance Systems (ADAS) have been proposed and introduced under the development of sensing technology and the issue of driving safety. But many kinds of ADASs have a specific threshold to control the alarm or some support. This is decided based on the experimental or mathematical calculations in terms of the optimization of the human-machine interface of each system. But almost all of the systems (especially warning systems) have just a single threshold value to issue the warning, and the driving performance of drivers fluctuating in real time is not considered. In this study, we proposed a quantification method of riding performance and performed the logistic regression analysis for the collision prediction model based on riding performance to optimize the warning threshold of ADAS. For this study, 64 test subjects (Mean age = 22.14, S.D. = 3.71) participated in the experiments using simulator. Experiments were conducted for three risk events (left-angle collision when a rider was driving on priority road or driving on non-priority road, and right turning collision) and dummy events with the same road environment without risky situations. We proposed a quantification method of riding performance through the total sum of a product of the generalized value of riding behaviours. We also proposed the logit model, which can be constructed in terms of the collision probabilities and riding performance, which is quantified using our proposed method. In the logit model, collision occurrence was used as the dependent variable and riding performance was used as the independent variable for logistic regression analysis to clarify the condition where the probability of collision increases. Finally, we proposed a concept of the setting method of threshold value for the warning timing of ADAS according to the rider’s performance level based on collision probabilities during each riding performance.
... There are some efforts to optimize ACC settings to maximize traffic flow [9], [10]. However, these works are based on off-line optimization which does not cope well with dynamically changing traffic conditions. ...
An Adaptive Cruise Control (ACC) system allows vehicles to maintain a desired headway distance to a preceding vehicle automatically. ACC is increasingly adopted by recently available commercial vehicles. Recent research demonstrates that effective use of ACC can improve the traffic flow by adjusting the headway distance in response to dynamically changing traffic conditions. In this paper, we demonstrate that state-of-the-art real-time ACC systems may perform poorly in highway segments with on-ramps because their simple model based only on the traffic conditions of the main road does not take into account the dynamics of merging traffic in determining the optimal headway distance. We propose D-ACC, a dynamic adaptive cruise control system based on deep reinforcement learning that effectively adapts the headway distance according to dynamically changing traffic conditions of both the main road and merging lane to optimize traffic flow. Extensive simulations are performed with a combination of a traffic simulator SUMO and vehicle-to-everything communication (V2X) network simulator Veins under various traffic scenarios. We demonstrate that D-ACC improves the traffic flow by up to 70% compared with a state-of-the-art real-time ACC system in a general highway segment with an on-ramp.
... There are few studies (Mba and Novara, 2016;Liu et al., 2017), which advance towards this direction and propose the offline optimization of the (constant) ACC system parameters so as to maximize the traffic flow efficiency. They demonstrate that appropriate (optimal) ACC settings may indeed lead to improved traffic performance. ...
An ACC (Adaptive Cruise Control)-based traffic control strategy is presented, which improves the motorway traffic flow efficiency by changing in real time the driving behaviour (specifically the employed time-gap and the acceleration strength) of ACC-equipped vehicles in motorway sections according to the corresponding traffic conditions. The control strategy comprises three distinct actions: (i) gradual decrease of ACC time-gaps at near-capacity traffic in order to increase capacity; (ii) minimum time-gaps and (iii) acceleration increase, both at the very vicinity of active bottlenecks in order to increase the discharge flow. The behaviour and impact of the control strategy, and of each of its parts separately, are demonstrated for different ACC penetration rates via microscopic simulation applied to a real motorway stretch. The simulation results show that, even for low penetration rates of ACC-vehicles, the proposed strategy leads to sensible improvements regarding the average vehicle delay and fuel consumption by delaying the onset of congestion (thanks to increased capacity); and by speeding up its dissolution (thanks to the mitigation of capacity drop).
... In the face of severe traffic accidents and environmental issues, intelligent transportation systems (ITSs) and advanced driver assistance systems (ADASs) have been proposed worldwide. ADASs are systems aimed at enhancing active safety and providing a better driving experience for drivers; however, they have recently aroused concerns from a wide range of sources [1]. Adaptive cruise control (ACC) is a newly developed ADAS system and has been an important milestone in the history of driving assistance [2,3]. ...
In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.
In this study, vehicle queuing was investigated at intersections to propose an eco-driving strategy to improve vehicle energy consumption and traffic efficiency in urban traffic environments. The proposed design approach can be applied to electric vehicles, and the control framework is categorized into two layers. In the upper layer, the speed of the host vehicle is planned offline, and in the lower layer, the required control variable acceleration is determined. First, the energy optimization problem of electric vehicles passing through an intersection was constructed, and the planning vehicle speed was obtained based on the genetic algorithm (GA). Next, the speed tracking controller and distance tracking controller were designed using sliding mode control (SMC) to ensure that the vehicle can track the planning speed with safe vehicle spacing. Finally, combined with specific cases, the energy-saving effect of the proposed method in the single-vehicle scenario, and the presence of manual driving vehicles in front- and multi-vehicle driving scenarios were studied. The results revealed that the GA-based single-vehicle speed planning method reduced energy consumption by up to 16% compared with the rule-based speed planning method. Furthermore, compared with the intelligent driver model (IDM) and adaptive cruise control (ACC) methods, the GA fleet speed planning method based on V2X communication can reduce average fleet energy consumption by 26% and 24%, respectively, and improve intersection traffic efficiency. The results of the sensitivity analysis of factors affecting planned speed revealed that vehicles passing through intersections at a steady speed exhibited superior economic performance. Finally, hardware-in-the-loop (HIL) testing was performed to verify the effectiveness of the controller under real-time conditions.
In the context of Industry 4.0, connected vehicles have become one of the trending innovations. The novel concept of this research is to successfully integrate real-time network protocols into automated guided vehicles (AGVs), which are used for various tasks in logistics and supply chain, such as material transportation. Firstly, the cutting-edge techniques of EtherCAT networks are introduced as a background and urgent topic for both industries and researchers. Then, several enhanced mechanical designs are described to embed the required hardware into the current platform. Thirdly, theoretical development and mathematical computations regarding the mechanical design are launched to release the proper parameters. In this stage, a dual loop, including the low-level controller and high-level controller, is primarily innovated. The structure of cross-coupling control is suggested to be implemented due to its ability to compensate for uncertain errors. In addition, each active wheel is driven by a proportional-integral-velocity (PIV) scheme to minimize tracking errors. To certify our approach, validations in both laboratory and experimental scales are fulfilled under the same conditions. The comparative performance between EtherCAT-based cross-coupling (EbC) and pulse generation-based control (PGbC) is exemplified in tracking linear and circular trajectories. The contributions of this work are (i) presenting the first idea to embed the EtherCAT-based protocol as the communication network, (ii) demonstrating the mechanical implementation in the current vehicle platform, and (iii) establishing advanced techniques of dual loop controller.
The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver's response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems.
Consensus-based Speed Advisory System (CSAS) is used to recommend a consensus speed to a group of vehicles for specific application purposes, such as minimizing emissions or energy consumption. To remedy data privacy concerns for speed advisory services, the latest works have investigated how to get an optimal speed in a privacy-preserving manner. However, almost all the designs are based on a centralized architecture, which could still meet service trust issues, such as that a random speed could be recommended when the central server meets cyber incursion attacks. To address the problem, in this paper we propose BSAS, a trustworthy and privacy-preserving CSAS over the blockchain. Specifically, BSAS follows a fully decentralized architecture with cryptographic primitives to guarantee service trust and data privacy. Moreover, to encourage vehicles to participate in the service computing process, a value-driven incentive mechanism is also employed. We present the detailed design and implementation of BSAS, and our emulation results show that the proposed BSAS can achieve promising system performance in terms of real-time speed recommendation in a trustworthy and privacy-preserving way.
An accurate vehicle trajectory prediction promotes understanding of the traffic environment and enables task criticality assessment in advanced driver assistance systems (ADASs) in autonomous vehicles and intelligent connected vehicles. Nevertheless, conventional prediction models are characterized by low prediction accuracy, the inability of long-term prediction, and a single-road section adaptation. To tackle these limitations, this study proposes a trajectory prediction model based on a deep encoder–decoder and a deep neural network (DNN). One modification included introducing an attention mechanism into the traditional encoder–decoder framework. Overall, 1794,1400,2100 trajectory samples from highways, intersections, and roundabouts are used to train the proposed framework and obtain optimal deep encoder–decoder architectures for different road section types. Since the experiments revealed no significant advantages of using the attention mechanism in deep encoder–decoder, the mechanism is not included in the optimal architecture. Next, to achieve higher prediction accuracy and better long-term prediction capability, different DNN structures are tested as trajectory correction networks, and the optimal DNN structure is selected. Finally, the experiments are conducted using the proposed deep encoder–decoder framework and the optimal DNN. The results show that the proposed model reaches 92.87%, 86.65%, and 89.15% average trajectory fit ratio (TFR) on a highway, intersection, and a roundabout, respectively. Therefore, the model enables accurate long-term predictions of vehicle trajectories in these road segments. The proposed model and presented results provide a basis for ADASs’ trajectory prediction algorithms.
Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This paper investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this paper can avoid or mitigate the collision risk compared to the vehicle without this system.
Purpose
This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment.
Design/methodology/approach
The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions.
Findings
The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.
Originality/value
This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.
As the era of intelligent connected vehicles (ICVs) is approaching, a number of studies have investigated the potential benefits of ICVs, including the safety effects. Although previous studies agree that ICVs would significantly improve traffic safety, its quantified safety effects at different stages are still being debated. This study aims to estimate the ICVs’ safety effects by market penetration rate (MPR) adopting a meta-analysis approach. The results from the meta-analysis indicate that the number of conflicts is exponentially reduced as the MPR goes up. For example, compared to the environment without ICVs, 4.2% and 17.4% of conflicts would decrease at the MPR of 10% and 50%, respectively. The effects are more obvious at higher MPR—43.4% of conflicts are expected to decrease at the MPR of 90%. From the case study in the United States based on the meta-analysis, it is expected that the MPR would reach 17–20% in the near future (2025) and 40–48% in 2035. The anticipated reduction in the number of fatal crashes would be 5% and 13%, in 2025 and 2035, respectively. The findings from this study will be useful for both public and private sectors to establish strategic plans to promote ICVs and identify their benefits at different MPRs.
An intelligent speed advisory system can be used to recommend speed for vehicles travelling in a given road network in cities. In this paper, we extend our previous work where a distributed speed advisory system has been devised to recommend an optimal consensus speed for a fleet of Internal Combustion Engine Vehicles (ICEVs) in a highway scenario. In particular, we propose a novel optimisation framework where the exact format of each vehicle's cost function can be implicit, and our algorithm can be used to recommend multiple consensus speeds for vehicles travelling on different lanes in an urban highway scenario. Our studies show that the proposed scheme based on an improved whale optimisation algorithm can effectively reduce CO2 emission generated from ICEVs while providing different recommended speed options for groups of vehicles.
Currently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety and mobility challenges on highways enduring snowy winter weather. To address this pressing issue, this commentary proposes that the physical and digital infrastructures should be upgraded to take advantage of emerging technologies and facilitate the vehicle-infrastructure integration (VII), to better inform decision-makers at various levels. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. This commentary envisages the deployment of vehicle-to-everything (V2X) technologies to bring about transformative changes and substantial benefits in terms of enhanced winter safety and mobility on highways. At the center of the commentary is a conceptualized design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to connected infrastructure (CI) applications, to leverage the immensely expanded data availability fueled by better spatial and temporal coverage. The commentary also advances the idea that CI solutions can augment the sensing capabilities and confidence level of connected or autonomous vehicles. The application scenarios of VII system is then briefly explored, followed by some discussion of the paradigm shift towards V2X applications and a look to the future including some identified research needs in the arena of CI. This work aims to inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, because the specific challenges call for systematic, holistic, and multidisciplinary approaches accompanied by concerted efforts in the research, development, pilot testing, and deployment of CI technologies.
The relevance of Electric Vehicles (EVs) and the overall market demands of the respective control units is in a never before leap all around the globe as seen from the news, business studies, research trends and technological innovations today. Compared to earlier years, the relevance of driving safety in EVs also gains special attention due to the unforeseen surge in promoting EVs by National, State and City administrations for better environment and societal changes in future. For EV, the scenario broadens to a wider landscape beyond the earlier passive safety design features, to a highly comfortable and safer possible road travel. Safety enhancements can be experimented and implemented on EVs in a reliable way with higher end control of the dynamics, stability and optimised utilisation of individual vehicle characteristics and driver behaviours. In this paper, an attempt is made to scrutinise different control design approaches and possible solution paths experimented upon in the past and currently for EV as seen in the published literature. The quest is also to explore optimisation strategies in an organised way to ensure best possible driving safety along with passenger safety in EVs.
Cooperative adaptive cruise control (CACC) vehicle string operations have the potential to improve significantly the mobility and energy consumption performance of congested freeway corridors. This study examines the impact of CACC string operations on vehicle speed and fuel economy on the 13-mi SR-99 corridor, near Sacramento, CA. It extends the existing body of knowledge by performing a multi-scenario simulation analysis of the freeway corridor. A simulation study evaluated the performance of the corridor under various CACC market penetration scenarios and traffic demand inputs. The CACC string operation was also analyzed when vehicle awareness device (VAD) and CACC managed lane (ML) strategies were implemented. The case study revealed that the average vehicle speed increased by 70% when the CACC market penetration increased from 0% to 100%. The highest average fuel economy, expressed in miles per gallon (mpg), was achieved under the 50% CACC scenario where mpg was 27. This was 10% higher than the baseline scenario. However, when the CACC market penetration was 50% or higher, the vehicle fuel efficiency only had minor increases. When CACC market penetration reached 100%, the corridor allowed 30% more traffic to enter the network without experiencing reduced average speed. Results also indicate that the VAD strategy increased the speed by 8% when the CACC market penetration was 20% or 40%, while there was a minor decrease in mpg. The ML strategy decreased the corridor performance when implemented alone.
Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to ~450 s for ETO.
This paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver's behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time dri-ver's compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6–11% of safety improvements and 5–16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.
This study used microscopic simulation to estimate the effect on highway capacity of varying market penetrations of vehicles with adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC). Because the simulation used the distribution of time gap settings that drivers from the general public used in a real field experiment, this study was the first on the effects of ACC and CACC on traffic to be based on real data on driver usage of these types of controls. The results showed that the use of ACC was unlikely to change lane capacity significantly. However, CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages. The capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awareness devices so that they could serve as the lead vehicles for CACC vehicles.
This paper describes the use of microscopic simulation to estimate the effect of varying market penetrations of adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) on highway capacity. The distribution of time gap settings that drivers from the general public used in a real field experiment were used in the simulation, making this the first study of the effects of ACC and CACC on traffic to be based on real data on driver usage of ACC and CACC. The results show that the use of ACC is unlikely to change lane capacity significantly. However, CACC is able to greatly increase capacity after its market penetration reaches moderate to high percentages. The capacity increase can be accelerated by equipping non-ACC vehicles with Vehicle Awareness Devices so that they can serve as the lead vehicles for CACC vehicles.
A smart driving system (providing both safety and fuel-efficient driving advice in real time in the vehicle) was evaluated in real-world on-road driving trials to see if any measurable beneficial changes in driving performance would be observed. Forty participants drove an instrumented vehicle over a 50-min mixed-route driving scenario. Two conditions were adopted: one is a control with no smart driving feedback offered and the other is with advice being presented to the driver via a smartphone in the vehicle. Key findings from the study showed a 4.1% improvement in fuel efficiency when using the smart driving aid, importantly with no increase in journey time or reduction in average speed. Primarily, these efficiency savings were enabled by limiting the use of lower gears (facilitated by planning ahead to avoid unnecessary stops) and an increase in the use of the fifth gear (as advised by the in-vehicle system). Significant and important changes in driving safety behaviors were also observed, with an increase in mean headway to 2.3 s and an almost threefold reduction in time spent traveling closer than 1.5 s to the vehicle in front. This paper has shown that an in-vehicle smart driving system specifically developed and designed with the drivers' information requirements in mind can lead to significant improvements in driving behaviors in the real world on real roads with real users.
In this contribution, we put forward a modelling framework for generic Advanced Driver Assistance Systems (ADAS) based on rolling horizon optimal control and design control algorithms for an Ecological Adaptive Cruise Control (EcoACC) system under this framework. The accelerations of EcoACC vehicles are determined by minimizing some predicted cost, and the optimal control problem is solved using a dynamic programming approach. The proposed algorithm is applied on a single lane ring road to examine the impacts of the EcoACC system employing the Eco-driving strategy comparison with a system employing an Efficient-driving strategy. Simulation results show that the Eco-driving strategy results in smoother vehicle behaviour compared to the driving strategies that only consider travel efficiency (Efficient-driving strategy). At the macroscopic level, the Eco-driving strategy results in a lower speed and lower flow at free traffic conditions, but a higher speed and higher flow at moderate congested conditions compared to the Efficient-driving strategy. From an environment perspective, the Eco-driving strategy results in a lower spatial CO2 emission rate. However, in the ring-road scenario where the demand is not fixed, the impact of the EcoACC system on total CO2 emissions is negative at moderate congested conditions, due to the high flow it supports.
Collision warning and collision avoidance systems are emerging automotive safety technologies that assist drivers in avoiding rear-end collisions. Their function is to allow the driver enough time to avoid the crash and yet avoid annoying the driver with alerts perceived as occurring too early or unnecessary. The aim of this paper was analyzing the driver's behavior in order to define effective driver assistance systems which can be readily accepted by the driver. A study was performed with an interactive fixed-base driving simulator. A sample of 32 drivers drove on a two-lane rural road. Four different driving traffic conditions were implemented. The data recorded during the tests were analyzed to assess the safety distances required by the driver during a car-following situation. Based on the risk perception of the driver a new collision warning algorithm was developed.
ABSTRACT Road safety is a major concern in all countries and,large efforts are constantly dedicated to create safer traffic environments. Today, increasing attention is turned towards active safety improving,countermeasures,that improve,road safety by reducing,accident risks. Such active countermeasures,include Advanced Driver Assistance Systems (ADAS). To assure that these new applications result in real safety improvements, a priori estimations of safety effects are needed. This paper considers estimation of the safety effects of ADAS through traffic simula- tion. Requirements imposed,on a,traffic simulation model,to be used for ADAS evaluation is presented and a car-following model to be used in simulations including ADAS-equipped ve- hicles is proposed. ADAS have an impact,on traffic through the system functionalities of the ADAS and through changes in driver behaviour for ADAS-equipped vehicles. Driver behav- iour for ADAS-equipped vehicles has ,usually not been considered in previous ,simulation
The effects of Cooperative Adaptive Cruise Control (CACC) on traffic flow is an important issue as traffic flow stability, capacity and safety are concerned. In contrast to most research we focus on traffic flow stability. We use the Intelligent Driver Model and CACC algorithms to assess the effects. A recently field-tested and CACC-based advisory system is also evaluated as an intermediate solution. It is found that CACC can quickly damp shockwaves at lower penetration rates (50%) and that shockwaves move faster.
More and more vehicles are being equipped with Automatic Emergency Braking (AEB) systems. These systems intend to help the driver avoid or mitigate accidents by automatically applying the brakes prior to an accident. Initially only rear-end collision were addressed but over time more accident types are incorporated and brakes are applied earlier and stronger, in order to increase the velocity reduction before the accident occurs. This paper describes one of the latest AEB systems called Collision Warning with Full Auto Brake and Pedestrian Detection (CWAB-PD). It helps the driver with avoiding both rear-end and pedestrian accidents by providing a warning and, if necessary, automatic braking using full braking power. A limited set of accident scenarios is selected to illustrate the theoretical and practical performance of this system. It is shown that the CWAB-PD system can avoid accidents up to 35 km/h and can mitigate accidents achieving an impact speed reduction of 35 km/h. To the best of the authors knowledge CWAB-PD is the only system on the market that automatically can avoid accidents with pedestrians.
With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as the basis of an ACC implementation in real cars. The model is based on the intelligent driver model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.
This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities while drivers were engaged in cell phone conversations in a driving simulator.
Tactile and auditory collision warnings have been shown to improve braking response time (RT) in rear-end collision situations. However, it is not clear how effective these warnings are when the driver is engaged in attentionally demanding secondary tasks, such as talking on a cell phone.
Sixteen participants in a driving simulator experienced three collision warning conditions (none, tactile, and auditory) in three conversation conditions (none, simple hands free, complex hands free). Driver RT was captured from warning onset to brake initiation (WON2B).
WON2B times for auditory warnings were significantly larger for simple conversations compared with no conversation (+148 ms), whereas there was no significant difference between these conditions for tactile warnings (+53 ms). For complex conversations, WON2B times for both tactile (+146 ms) and auditory warnings (+221 ms) were significantly larger than during no conversation. During complex conversations, tactile warnings produced significantly shorter WON2B times than no warning (-141 ms).
Tactile warnings are more effective than auditory warnings during both simple and complex conversations.
These results indicate that tactile rear-end collision warnings have the potential to offset some of the driving impairments caused by cell phone conversations.
Many new in-vehicle systems focus on accident prevention by facilitating the driving task. One such driving aid is an in-vehicle collision avoidance warning system (IVCAWS), used to alert the driver to an impending collision. Our study evaluated the effects of an imperfect IVCAWS both on driver headway maintenance and on driver behavior in response to warning system errors. Our results showed that drivers tend to overestimate their headway and consequently drive with short and potentially dangerous headways, and that IVCAWSs are a useful tool for educating drivers to estimate headway more accurately. Moreover, our study showed that after a relatively short exposure to the system, drivers were able to maintain longer and safer headways for at least six months. The practical implications of these results are that the use of an IVCAWS should be considered for inclusion in driver education and training programs.
The effectiveness of a headway measuring and recording device was evaluated in terms of its ability to increase drivers' car-following distance. Forty-three drivers first drove for approximately 3 weeks without headway feedback and then for approximately 3 more weeks with immediate time headway (THW) feedback. Whenever the THW decreased to 1.2 s or less a red warning light came on, and whenever the THW decreased further to 0.8 s or less a buzzer was also sounded. The results showed that prior to receiving THW information,drivers drove at shorter headways than after they received that information. The effect of the feedback was to reduce the time spent in short headways (< or = 0.8 s) by approximately 25% (from 20% to 15% of the time) and to increase the time spent in safer longer headways (>1.2 s) by approximately 20% (from 57% to 65% of the time). The effect was similar for younger and older drivers, for male and female drivers, for urban and highway speeds, and for daytime and nighttime driving. An immediate application of these findings is to install headway feedback displays to drivers so that they may maintain safer headway distances than they do currently.
Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles
A Driver Assistance System for Continuous Support continuously evaluates the status of the host vehicle as well as the surrounding traffic based on information from on-board sensors. When the system detects a hazard, it issues a warning to the driver, depending on the degree of the hazard. The effects of this system on driver behaviour and acceptance were evaluated in a field trial carried out in 2013. Twenty-four drivers took part in test drives with a within-subject design along a 53 km test route containing motorway and rural-road sections. Driving data was logged and the test drivers were observed by means of an in-car observation method (Wiener Fahrprobe); in this case by two observers in the car along with the driver. Questionnaires were used to assess the drivers’ comprehension of and reaction to the system. The system was successful in affecting driver behaviour in terms of lower speed when negotiating curves. Positive effects were found in the form of better speed adaptation to the situation during driving with the system activated. Also, lane choice and lane change improved with the system on. When it came to speed limit compliance, driving speed in general and longitudinal and lateral positioning, no effects could be found. No major differences were found regarding distance to the vehicle in front, overtaking manoeuvres, stopping behaviour at intersections, driving against yellow at traffic lights and interaction behaviour with other road users while driving with or without the system. On the negative side, it was noted that only during driving with the system activated did the test drivers make turns at intersections at too high speeds. In addition, more errors associated with dangerous distance to the side were observed with the system activated. In terms of the emotional state of the driver, the only difference found was that the drivers felt an increase in irritation. Regarding subjective workload, the drivers only assessed one item, i.e. whether their performance decreased statistically significantly while driving with the system. The test drivers were of the opinion that the system was useful, and that it would enhance safety especially in overtaking manoeuvres on motorways. The blind-spot warning was found especially useful in the overtaking process. The drivers appreciated the fact that the system did not give information all the time.
This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.
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.
Forward Collision Warning Systems (FCWS) have been designed to enhance road safety by reducing the number of rear-end collisions. Nevertheless, little is known about how drivers adapt their behaviour over time when using this kind of system. In addition, these systems are expected to aid particularly distracted drivers. However, previous research has suggested that the effectiveness of the system could depend on the difficulty level of the secondary task. The objective of this study on driving simulator was twofold. Firstly, it consisted in evaluating the behavioural adaptation to an FCWS as well as analysing the possible consequences of driving without the system after a short period of adaptation. Secondly, it was to evaluate the effectiveness of the system according to two different difficulty levels of a cognitive secondary task. The results showed that drivers adapted their behaviour positively when the system was introduced. Nevertheless, both the effectiveness and the behavioural adaptation in the short term were dependent on the cognitive load induced by the secondary task. These findings suggest that the warning needs some attentional resources to be processed. Finally, no negative or transfer effect was observed following the removal of the system after a short period of adaptation.
The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.
In-vehicle technologies and co-operative services have potential to ease congestion problems and improve traffic safety. This paper investigates the impact of infrastructure-to-vehicle co-operative systems, case of CO-OPerative SystEms for Intelligent Road Safety (COOPERS), on driver behavior. Thirty-five test drivers drove an instrumented vehicle, twice, with and without the system. Data related to driving behavior, physiological measurements, and user acceptance was collected. A macro-level approach was used to evaluate the potential impact of such systems on driver behavior and traffic safety. The results in terms of speeds, following gaps, and physiological measurements indicate a positive impact. Furthermore, drivers' opinions show that the system is in general acceptable and useful.
This contribution presents the results of a microscopic traffic simulation study of the potential effects of an overtaking assistant for two-lane rural roads. The overtaking assistant is developed to support drivers in judging whether or not an overtaking opportunity can be accepted based on the distance to the next oncoming vehicle. Drivers have been found to consider this to be a difficult part of an overtaking manoeuvre. The assistant’s effects on traffic efficiency, driver comfort and road safety have been investigated using traffic simulation. The results indicate that this type of overtaking assistant can provide safety benefits in terms of increased average time-to-collision to the next oncoming vehicle during overtaking manoeuvres. This safety benefit can be achieved without negative consequences for traffic efficiency and driver comfort. A driver assistance system that supports the distance judging part of overtaking manoeuvres can therefore contribute to improved traffic conditions on the two-lane rural roads of the future.
The study addressed the role of familiarization on a driving simulator with a forward collision warning (FCW) and investigated its impact on driver behavior.
Drivers need a good understanding of how an FCW system functions to trust it and use it properly. Theoretical and empirical data suggest that exploring the capacities and limitations of the FCW during the learning period improves operating knowledge and leads to increased driver trust in the system and better driver-system interactions.The authors tested this hypothesis by comparing groups of drivers differing in FCW familiarity.
During the familiarization phase, familiarized drivers were trained on the simulator using the FCW, unfamiliarized drivers simply read an FCW manual, and control drivers had no contact with the FCW. During the test, drivers drove the simulator and had to interact with traffic; both familiarized and unfamiliarized drivers used the FCW, whereas controls did not.
Simulator familiarization improved driver understanding of FCW operation. Driver-system interactions were more effective: Familiarized drivers had no collisions, longer time headways, and better reactions in most situations. Familiarization increased trust in the FCW but did not raise system acceptance.
Familiarization on the simulator had a positive effect on driver-system interactions and on trust in the system. The limitations of the familiarization method are discussed in relation to the driving simulator methodology.
Practicing on a driving simulator with driving-assistance systems could facilitate their use during real driving.
This paper presents a new collision warning (CW) algorithm for rear-end collisions. Considering the large number of traffic accidents that result due to driver errors or situations that are unpredictable for the driver, many CW Algorithms were developed in the past years. However, these algorithms did not adequately take into account vehicles with an adaptive cruise control (ACC) System. This paper aims to modify these algorithms assuming the presence of an ACC system and to develop a new algorithm considering human factors and maneuvers of vehicles with ACC in critical situations
The effects of a driver assistance system for keeping safe speed and safe distance (referred to as SASPENCE) on driver behaviour, reactions and acceptance were evaluated in a test carried out in 2006. Twenty test drivers, recruited by ads, drove two times (once with the system off and once with the system on) in real traffic conditions along a 50 km long test route containing urban and rural roads and motorway sections outside Turin, Italy. Driving data was logged and the test drivers were observed by means of an in-car observation method, in this case by two observers riding along in the car with the driver. Driver opinions were collected through questionnaires. The findings show positive effects of the system in terms of fewer alarm situations, shorter alarm lengths, shorter reaction times, increased headway and better interactions with vulnerable road users at intersections. On the negative side, driver performance worsened slightly, the number of centre line crossings increased, there was worse facilitating behaviour with regard to other drivers and harder braking at traffic lights. No major effect on speed behaviour of the driver, lane choice, lane keeping, lane change, overtaking, red running, use of turning indicator and workload was found.
This paper introduces Simulation of Intelligent TRAnsport Systems (SITRAS), a massive multi-agent simulation system in which driver-vehicle objects are modelled as autonomous agents. The simulation outputs can be used for the evaluation of Intelligent Transport Systems applications such as congestion and incident management, public transport priority and dynamic route guidance. The model concepts and specifications, and the first applications of the model in the area of incident modelling in urban arterial networks were described in previous publications. This paper presents the details of the lane changing and merging algorithms developed for the SITRAS model. These models incorporate procedures for ‘forced’ and ‘co-operative’ lane changing which are essential for lane changing under congested (and incident-affected) traffic conditions. The paper describes the algorithms and presents simulation examples to demonstrate the effects of the implemented models. The results indicate that only the forced and cooperative lane changing models can produce realistic flow-speed relationships during congested conditions.
This paper presents data, collected from video-recording, on the microscopic details of merging and weaving manoeuvres under congested traffic conditions. Based on these observations, a classification of the manoeuvres into free, forced and cooperative lane changes is proposed. A new lane change model is developed, incorporating explicit modelling of vehicle interactions using intelligent agent concepts. The model was implemented in the ARTEMiS traffic simulator, and several hypothetical test studies were conducted to demonstrate the capabilities of the new model. The results show that the model is able to reproduce the observed behaviour of individual vehicles in terms of speed, gap acceptance and conflict-resolution in all three types of lane change manoeuvres, and hence, it is able to simulate highly congested flow conditions in a realistic manner. The macroscopic results in terms of speed-flow relationship are close to the typical expected results. The model can simulate both freeways and signalised urban arterial networks.
We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration.We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the, respectively, underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multi-anticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.
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.
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.
American association of state highway and transportation officials (AASHTO)
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Alert Algorithm Development Program: NHTSA Rear-end collision Alert Algorithm
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Transport information and control systems -Forward Vehicle Collision Warning Systems -Performance Requirements and Test Procedures
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Cooperative Adaptive Cruise Control: Human Factors Analysis
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Forward Collision Warning Requirements Project: Refining the CAMP Crash Alert Timing Approach by Examining ''Last-Second" Braking and Lane Change Maneuvers Under Various Kinematic Conditions
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Integrated vehicle-based safety systems (IVBSS) light vehicle field operational test independent evaluation
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