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The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts. [ABSTRACT FROM AUTHOR]

Estimating the time until impending collision (time-to-collision, TTC) of approaching or looming individuals and maintaining a comfortable distance from others (interpersonal distance, IPD) are commonly required in daily life and contribute to survival and social goals. Despite accumulating evidence that facial expressions and gaze direction interactively influence face processing, it remains unclear how these facial features affect the spatiotemporal processing of looming faces. We examined whether facial expressions (fearful vs. neutral) and gaze direction (direct vs. averted) interact on the judgments of TTC and IPD for looming faces, based on the shared signal hypothesis that fear signals the existence of threats in the environment when coupled with averted gaze. Experiment 1 demonstrated that TTC estimates were reduced for fearful faces compared to neutral ones only when the concomitant gaze was averted. In Experiment 2, the emotion-gaze interaction was not observed in the IPD regulation, which is arguably sensitive to affective responses to faces. The results suggest that fearful-averted faces modulate the cognitive extrapolation process of looming motion by communicating environmental threats rather than by altering subjective fear or perceived emotional intensity of faces. The TTC-specific effect may reflect an enhanced defensive response to unseen threats implied by looming fearful-averted faces. Our findings provide insight into how the visual system processes facial features to ensure bodily safety and comfortable interpersonal communication in dynamic environments. [ABSTRACT FROM AUTHOR]

The accurate estimation of time‐to‐collision (TTC) is essential for the survival of organisms. Previous studies have revealed that the emotional properties of approaching stimuli can influence the estimation of TTC, indicating that approaching threatening stimuli are perceived to collide with the observers earlier than they actually do, and earlier than non‐threatening stimuli. However, not only are threatening stimuli more negative in valence, but they also have higher arousal compared to non‐threatening stimuli. Up to now, the effect of arousal on TTC estimation remains unclear. In addition, inconsistent findings may result from the different experimental settings employed in previous studies. To investigate whether the underestimation of TTC is attributed to threat or high arousal, three experiments with the same settings were conducted. In Experiment 1, the underestimation of TTC estimation of threatening stimuli was replicated when arousal was not controlled, in comparison to non‐threatening stimuli. In Experiments 2 and 3, the underestimation effect of threatening stimuli disappeared when compared to positive stimuli with similar arousal. These findings suggest that being threatening alone is not sufficient to explain the underestimation effect, and arousal also plays a significant role in the TTC estimation of approaching stimuli. Further studies are required to validate the effect of arousal on TTC estimation, as no difference was observed in Experiment 3 between the estimated TTC of high and low arousal stimuli. [ABSTRACT FROM AUTHOR]

Traffic conflict techniques enable a comprehensive assessment of traffic safety analysis. Formal methods allow the identification of factors that contribute to traffic safety issues and provide evidence of potential safety degradation. As such, formal methods provide a novel way to model traffic rules and verify road users' compliance. The paper proposes formalizing a traffic safety rule in differential dynamic logic and using KeYmaera theorem prover for verification. This rule considers time-to-collision (TTC), space headway (SHW), and shockwave speed (SWV). To validate the effectiveness of this rule in realistic traffic scenarios, we conducted a study using calibrated microsimulation data from the SR528 highway in Orlando, Florida. Our analysis examined the TTC, SHW, and SWV values for vehicle platoons on the highway and demonstrated how smaller TTC and SHW values indicate shockwaves and subsequent conflicts. Furthermore, we observed that shockwave speed could contribute to traffic conflicts by enabling evasive actions such as sudden braking or lane changes as the risk of collisions increases. By highlighting these findings, we aim to provide valuable insights into the real-world applicability of formal methods for traffic safety and their potential in promoting safer driving practices that can help create reliable autonomous vehicle control systems.

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper.

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper. [ABSTRACT FROM AUTHOR]

In India, the crossing conflicts between right-turn vehicles of minor road traffic and conflicting vehicles on major roads have become more severe at unsignalized T-intersections in recent times. Due to the rapid growth of vehicular traffic, including motorized two-wheelers, auto-rickshaws, etc., results in an increase in right-turn crossing conflicts (RTCC) at unsignalized T-intersections. The severity of right-turn vehicles is related to the characteristics of both right-turning and conflicting through vehicles. Therefore, this study examines the effect of vehicle types on RTCC. The RTCC are observed using time-to-collision (TTC), at five unsignalized T-intersections in three different cities in India. The RTCC are categorized into critical RTCC and non-critical RTCC based on TTC threshold values obtained by the k-means clustering algorithm. A Generalized Poisson Regression model was developed using Python software. The study results revealed that the presence of higher composition of two-wheelers, auto-rickshaws, and cars in right-turn and conflicting through vehicles significantly influences the severity of RTCC. Also, the model results concluded that the speeds of right-turn and conflicting through vehicles, conflicting through traffic, right-turn traffic, vehicle gap, waiting time, and abnormal driving paths significantly affect the RTCC at unsignalized T-intersections. The findings of this study help traffic engineers and safety experts identify the critical unsignalized T-intersections using the number of right-turn crossing conflicts.

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.

In this paper, a novel two-lane car-following model considering driver's visual effect is proposed. Changes in driver behaviour are directly stimulated from visual information and an improved time-to-collision (TTC) calculation is presented. Using TTC, stimuli are formulated for describing the influence of leading vehicles. Besides, the model is established based on the optimal velocity model framework. The stability of the proposed model is discussed by theoretical analysis and numerical simulation. Moreover, the influences of parameters and the comparison between models are investigated by simulations, which show that the proposed model can effectively describe the influences. Finally, the model is calibrated and verified by NGSIM trajectory data, which shows that the proposed model fitting effect can be improved by 7.78%, 44.96%, and 32.07% respectively compared with other three models. This study may provide a basis for the design of control strategies for future intelligent connected vehicles in multi-lane environments. [ABSTRACT FROM AUTHOR]

In India, the crossing conflicts between right-turn vehicles of minor road traffic and conflicting vehicles on major roads have become more severe at unsignalized T-intersections in recent times. Due to the rapid growth of vehicular traffic, including motorized two-wheelers, auto-rickshaws, etc., results in an increase in right-turn crossing conflicts (RTCC) at unsignalized T-intersections. The severity of right-turn vehicles is related to the characteristics of both right-turning and conflicting through vehicles. Therefore, this study examines the effect of vehicle types on RTCC. The RTCC are observed using time-to-collision (TTC), at five unsignalized T-intersections in three different cities in India. The RTCC are categorized into critical RTCC and non-critical RTCC based on TTC threshold values obtained by the k-means clustering algorithm. A Generalized Poisson Regression model was developed using Python software. The study results revealed that the presence of higher composition of two-wheelers, auto-rickshaws, and cars in right-turn and conflicting through vehicles significantly influences the severity of RTCC. Also, the model results concluded that the speeds of right-turn and conflicting through vehicles, conflicting through traffic, right-turn traffic, vehicle gap, waiting time, and abnormal driving paths significantly affect the RTCC at unsignalized T-intersections. The findings of this study help traffic engineers and safety experts identify the critical unsignalized T-intersections using the number of right-turn crossing conflicts. • Severity of right-turn crossing conflicts was proposed using K-means clustering. • Conflict severity models were developed using Generalized Poisson Regression. • Explored the effect of vehicle types on right-turn crossing conflicts' (RTCC's) severity. • Composition of vehicles and right-turn traffic has a significant impact on RTCC's severity. [ABSTRACT FROM AUTHOR]

To cross a road safely, pedestrians estimate the time remaining until an approaching vehicle arrives at their location (time-to-collision, TTC). For visually presented accelerated objects, however, TTC estimates are known to show a first-order pattern indicating that acceleration is not adequately considered. We investigated whether added vehicle sound can reduce these estimation errors. Twenty-five participants estimated the TTC of vehicles approaching with constant velocity or accelerating, from a pedestrian's perspective at the curb in a traffic simulation. For visually-only presented accelerating vehicles, the TTC estimates showed the expected first-order pattern and thus large estimation errors. With added vehicle sound, the first-order pattern was largely removed, and TTC estimates were significantly more accurate compared to the visual-only presentation. For constant velocities, TTC estimates in both presentation conditions were predominantly accurate. Taken together, the sound of an accelerating vehicle can compensate for erroneous visual TTC estimates presumably by promoting the consideration of acceleration. [ABSTRACT FROM AUTHOR]

The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Cars and trucks have different sizes and operation characteristics, but very few traffic conflict analysis studies considered different vehicle types at freeway merging sections. Thus, the main objective of this study is to analyze lane-changing conflicts between different vehicle types at freeway merging sections. Vehicle trajectories are extracted from the Unmanned Aerial Vehicle (UAV) video data which are collected in Shanghai, China. Time-to-collision (TTC) is utilized as the surrogate safety measure (SSM) to analyze lane-changing conflicts. Results show that TTC values of car-car conflicts are the smallest, while truck-truck conflicts have the largest TTC values. Although traffic conflicts frequently occur at the on-ramp and additional rightmost lane, the spatial distribution of lane-changing conflicts is significantly different between different vehicle types. The findings of this study are useful for transportation management agencies to design proper strategies to improve traffic safety at freeway merging sections. [ABSTRACT FROM AUTHOR]

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.

In this work, a simulation framework for virtual testing of autonomous driving functions under the influence of a fault occurring in a component is presented. The models consist of trajectory planning, motion control, models of actuator management, actuators and vehicle dynamics. Fault-handling tests in a right-turn maneuver are described, subject to an injected fault in the steering system. Different scenarios are discussed without and with a fault and without and with counteractions against the fault. The results of five scenarios for different criticality metrics are discussed. In the case of a fault without a counteraction, a pronounced lateral position deviation of the ego vehicle from the reference curve is observed. Furthermore, the minimal and hence most critical time-to-collision (TTC) and post-encroachment time (PET) values are calculated for each scenario together with a parameter variation of the initial position of a traffic agent. The minimum TTC values are lowest in the case of a fault without counteraction. For the lateral position deviation and the TTC, the counteractions cause reduced criticality that can become even lower than in the case without a fault, corresponding to a decrease in the dynamic behavior of the vehicle. For the PET, only in the case of a fault without counteraction, a non-zero value can be calculated. With the implemented testing toolchain, the automated vehicle and the reaction of the HAD function in non-standard conditions with reduced performance can be investigated. This can be used to test the influence of component faults on automated driving functions and help increase acceptance of implemented counteractions as part of the HAD function. The assessment of the situation using a combination of metrics is shown to be useful, as the different metrics can become critical in different situations.

Fusion of X-band Doppler radar and infrared sensors can offer a great advantage for close proximity time-to-collision (TTC) prediction in the field of autonomous robot navigation due to precise obstacle speed detection and direction sensing. Nevertheless, poor ranging performance from the infrared sensors may result owing to fluctuating reflectivity of the moving obstacle. The TTC prediction accuracy may also be further degraded when the obstacle’s trajectory is non-parallel to the robot’s heading direction due to uncertainty in the radar’s radiation pattern which typically increases at the side lobes of the antenna. Thus, to enhance the performance, we propose an exponential Gaussian Process Regression (E-GPR)-based prediction model which is able to approximate an unknown function in a probabilistic manner. The proposed method is validated via a series of experiments with an obstacle approaching the robot from different viewing angles with a speed ranging between 30 and 63 cm/s. Results demonstrate that the average TTC error based on the sensor fusion is 0.31s; but with the E-GPR method, the error is successfully reduced to 0.0937s, which is the largest error reduction when compared against other competing machine-learning models such as multilayer perceptron neural network, support vector machine and boosted tree.

Road transportation is a critical mode of transportation because of its mobility characteristics. Road construction work zones (WZ) are widespread on roads because of increased travel demand. Thus, it is necessary to study traffic safety for WZs and regular sections. In this study, traffic safety is analyzed in terms of the conflict probability at selected roads with WZ and without work zone (WWZ) sections along the same road. Vehicular trajectory data for three traffic flow levels (free flow, near capacity, and congestion) were extracted using a newly developed machine learning-based semiautomated trajectory extractor tool. The derived trajectory data from both sections were used to identify the leader-follower vehicle pairs using MATLAB. Two surrogate safety measures were estimated to compute the rear-end conflicts: time-to-collision and deceleration rate to avoid a collision. Hence, the variation of the rear-end conflicts was further examined using the generalized extreme value theory. It is found that the conflict probability is more in the WWZ section than the WZ section, which may be attributed to the variation in speed and acceleration. It is also found that the conflict probability for the WWZ and WZ sections is reduced as the angle between the two vehicles increases. The study results should help highway authorities to implement suitable safety measures to reduce conflicts and crashes in the work zones. [ABSTRACT FROM AUTHOR]

The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections. [ABSTRACT FROM AUTHOR]

In this work, a simulation framework for virtual testing of autonomous driving functions under the influence of a fault occurring in a component is presented. The models consist of trajectory planning, motion control, models of actuator management, actuators and vehicle dynamics. Fault-handling tests in a right-turn maneuver are described, subject to an injected fault in the steering system. Different scenarios are discussed without and with a fault and without and with counteractions against the fault. The results of five scenarios for different criticality metrics are discussed. In the case of a fault without a counteraction, a pronounced lateral position deviation of the ego vehicle from the reference curve is observed. Furthermore, the minimal and hence most critical time-to-collision (TTC) and post-encroachment time (PET) values are calculated for each scenario together with a parameter variation of the initial position of a traffic agent. The minimum TTC values are lowest in the case of a fault without counteraction. For the lateral position deviation and the TTC, the counteractions cause reduced criticality that can become even lower than in the case without a fault, corresponding to a decrease in the dynamic behavior of the vehicle. For the PET, only in the case of a fault without counteraction, a non-zero value can be calculated. With the implemented testing toolchain, the automated vehicle and the reaction of the HAD function in non-standard conditions with reduced performance can be investigated. This can be used to test the influence of component faults on automated driving functions and help increase acceptance of implemented counteractions as part of the HAD function. The assessment of the situation using a combination of metrics is shown to be useful, as the different metrics can become critical in different situations. [ABSTRACT FROM AUTHOR]

Distracted driving is the most common cause of traffic accidents. According to a World Health Organization report, the number of traffic accidents has been increasing in recent years. To address this issue, distracted-driving recognition is an important area of traffic safety research. However, distracted behavior may be a part of a driver’s regular tasks. For example, sometimes a delivery person must use his/her phone while driving. The use of walkie-talkies is required for container-truck drivers because they improve unloading efficiency and reduce the time cargo ships spend in a port, resulting in cost savings. While driving on the highway, it is sometimes necessary to tune the radio to receive an update on road conditions. Furthermore, drinking water is permitted while waiting for a traffic signal for an extended period of time. Therefore, for the distracted-driving alert system, the driving scenario is important. To address this issue, we present a novel framework herein that combines driving perception and driver behavior recognition to provide the driver with appropriate warnings. By combining driver perception and behavior recognition, our proposed framework can reduce false alerts. We also define different time-to-collision standards to achieve humane and effective warnings. We try to study various behaviors and define various time-to-collision standards for making safety-level decisions. For driving perception and driver behavior recognition, a modified convolutional neural network is used, which alerts the driver immediately.

Fusion of X-band Doppler radar and infrared sensors can offer a great advantage for close proximity time-to-collision (TTC) prediction in the field of autonomous robot navigation due to precise obstacle speed detection and direction sensing. Nevertheless, poor ranging performance from the infrared sensors may result owing to fluctuating reflectivity of the moving obstacle. The TTC prediction accuracy may also be further degraded when the obstacle's trajectory is non-parallel to the robot's heading direction due to uncertainty in the radar's radiation pattern which typically increases at the side lobes of the antenna. Thus, to enhance the performance, we propose an exponential Gaussian Process Regression (E-GPR)-based prediction model which is able to approximate an unknown function in a probabilistic manner. The proposed method is validated via a series of experiments with an obstacle approaching the robot from different viewing angles with a speed ranging between 30 and 63 cm/s. Results demonstrate that the average TTC error based on the sensor fusion is 0.31s; but with the E-GPR method, the error is successfully reduced to 0.0937s, which is the largest error reduction when compared against other competing machine-learning models such as multilayer perceptron neural network, support vector machine and boosted tree. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach - This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings - This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value - The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

Individuals with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) may exhibit driving difficulties due to cognitive impairments such as time perception difficulties, a construct related to the perception of time-to-collision (TTC). This study examined the timing abilities of drivers with ASD and ADHD. Sixty participants (nADHD = 20, nASD = 20, nTD = 20) completed a time reproduction task and a TTC estimation task in a driving simulator. Results indicated drivers with ASD were less precise in time reproduction across all time intervals and over-reproduced time at shorter intervals. Drivers with ASD produced larger TTC estimates when driving at a faster speed compared to typically developing drivers. Drivers with ASD, but not ADHD, appear to present difficulties in time estimation abilities. [ABSTRACT FROM AUTHOR]

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.

Estimating the time until impending collision (time-to-collision, TTC) of approaching or looming individuals and maintaining a comfortable distance from others (interpersonal distance, IPD) are commonly required in daily life and contribute to survival and social goals. Despite accumulating evidence that facial expressions and gaze direction interactively influence face processing, it remains unclear how these facial features affect the spatiotemporal processing of looming faces. We examined whether facial expressions (fearful vs. neutral) and gaze direction (direct vs. averted) interact on the judgments of TTC and IPD for looming faces, based on the shared signal hypothesis that fear signals the existence of threats in the environment when coupled with averted gaze. Experiment 1 demonstrated that TTC estimates were reduced for fearful faces compared to neutral ones only when the concomitant gaze was averted. In Experiment 2, the emotion-gaze interaction was not observed in the IPD regulation, which is arguably sensitive to affective responses to faces. The results suggest that fearful-averted faces modulate the cognitive extrapolation process of looming motion by communicating environmental threats rather than by altering subjective fear or perceived emotional intensity of faces. The TTC-specific effect may reflect an enhanced defensive response to unseen threats implied by looming fearful-averted faces. Our findings provide insight into how the visual system processes facial features to ensure bodily safety and comfortable interpersonal communication in dynamic environments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yamasaki and Nagai.)

• An enhanced motion- planning approach for automated driving systems (ADSs) that dynamically adjusts the vehicle motion state in advance is proposed. It is integrated with a hierarchical model, driving heterogeneity, and trajectory prediction for surrounding human-driven vehicles (HDVs). • A transformer-based long-term surrounding HDV trajectory-prediction model is developed, which outperforms existing methods in terms of the prediction horizon and accuracy. • An intelligent decision-making strategy for ADS motion planning is developed using the predicted results of the hierarchical model. The time-to-collision is used to evaluate the safety performance of ADS motion planning. The enhanced approach exhibits a significant ADS safety improvement. Navigating automated driving systems (ADSs) through complex driving environments is difficult. Predicting the driving behavior of surrounding human-driven vehicles (HDVs) is a critical component of an ADS. This paper proposes an enhanced motion-planning approach for an ADS in a highway-merging scenario. This method utilizes the results of two aspects: the driving behavior and long-term trajectory of surrounding HDVs, which are coupled using a hierarchical model that is used for the motion planning of an ADS to improve driving safety. An unsupervised clustering algorithm is utilized to classify HDV drivers into two categories: aggressive and normal, as part of predicting their driving behaviors. Subsequently, a logistic regression model is employed for driving style prediction. For trajectory prediction, a transformer-based model that concentrated parallelization computations on longer sequence predictions via a self-attention mechanism is developed. Based on the predicted driving styles and trajectories of the surrounding HDVs, an intelligent decision-making strategy is utilized for ADS motion planning. Finally, real-world traffic data collected from drones on a highway ramp in Xi'an is used as a case study to train and evaluate the proposed model. The results demonstrate that the proposed approach can predict HDVs merging trajectories with a mean squared error (MSE) smaller than 0.125 at 30 s away from the merging point, outperforming existing approaches in terms of predictable duration and accuracy. Furthermore, it exhibited safety improvements by adjusting the ADS motion state in advance with good predictive power for the surrounding HDVs' motion. For both normal and aggressive driving styles, the ADS trajectories reached optimal safety at a prediction horizon of at least 10 s and over 6.67 s, respectively, when evaluating both time-to-collision (TTC) and deceleration rate to avoid a crash (DRAC) metrics. This indicates that our proposed procedures have improved safety for AVs with long-term predictive capabilities (i.e., over 6 s) for surrounding HDVs. Interestingly, the safety performance does not continue to improve with the increase in prediction horizon once the optimal safety performance (highest TTC and lowest DRAC) has been achieved for both normal and aggressive styles. This suggests that an excessively long prediction horizon (e.g., 30 s) is not always beneficial for safety performance of ADS in our case study. When ADS encounter aggressive HDVs, having a predicted surrounding HDV trajectory can extremely reduce crash risk and maintain a safe situation. This demonstrates that planning ADS trajectories with HDV predictions has significant effects on safety, especially for aggressive driving styles as compared to normal driving styles. [ABSTRACT FROM AUTHOR]

Vehicular wireless communications require both congestion control to guarantee the availability of a fraction of the bandwidth for safety-related event-driven messages in emergency cases, and awareness control to adapt the beaconing activity to the application needs and surrounding traffic situation. Most current approaches either ignore the traffic situation and only adapt the beaconing rate to the channel congestion state or override the congestion control limits, leading to questionable results in both cases. In this paper, we conceive and validate a novel approach, combining both aspects. Based on distributed Network Utility Maximization (NUM), our algorithm satisfies the constraints on channel availability, whereas the safety of the surrounding traffic situation is captured with a time-to-collision metric, used to assign priorities in the optimal allocation problem. The performance of the proposed approach is validated and compared to other popular algorithms. Results show that our proposal automatically anticipates a potential increase in rate due to a critical safety situation, but does not interfere with the reserved bandwidth for safety applications.

The present study explored the intrinsic event-related potential (ERP) features of the effects of acute psychological stress on the processing of motion-in-depth perception using a dual-task paradigm. After a mental arithmetic task was used to induce acute psychological stress, a collision task was used to evaluate motion-in-depth perception. The error value and average amplitude of late slow waves (SW) were significantly larger for the earlier colliding spheres' than for the later colliding spheres. The P1 peak latency in the left occipital region was significantly shorter than that of the right occipital region in the motion-in-depth perception task. Compared to the control condition, the estimated value of residual time-to-collision and error value were significantly reduced, and the N1 peak amplitude and the SW averaged amplitude were significantly increased in the stress condition. Longer motion-in-depth time improved discrimination accuracy and decreased the investment of cognitive resources. Acute psychological stress increased behavioral performance and enhanced attention resources on the motion-in-depth perception task together with greater investment of cognitive resources. [ABSTRACT FROM AUTHOR]

• The stability of mixed traffic flow with CAVs is analyzed. • A safety evaluation system with multiple indicators is proposed. • A sensitivity analysis is presented based on a numerical simulation. Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications : The application of CAVs can improve the stability and safety of traffic flow. [ABSTRACT FROM AUTHOR]

While anecdotal evidence suggests the consideration of a constant time-to-collision (TTC) threshold in advanced driver assistance systems, yet the prospect of the warning criterion acting as a catalyst for the evaluation of conflict severities in non-lane based traffic environments needs further assessment. The current study attempts to provide a copula-based framework to capture the potential dependence between TTC and centerline separation between the interacting vehicles in staggered-following events according to lead vehicle type. Detailed trajectory data extracted from the video footage of urban roads were used for the purpose of this study. Considering negative association between the considered variables and checked by the performance measures, the bivariate Frank copula model is found acceptable to model the bivariate dependency structure between TTC and CS. In particular, the results of the study corroborate a pragmatic decreasing relationship of TTC with increasing CS and decrease in lead vehicle size- a finding which indicates that the risk level of riders in staggered-following scenario varies by the type of front vehicle ahead and the lateral positioning of the vehicles. Based on the results presented in this paper, our recommendation is to use different minimum TTC thresholds for different centerline separations and leader-follower pairs to suitably evaluate traffic safety in such non-lane based traffic environments. [ABSTRACT FROM AUTHOR]

Various surrogate safety measures have been introduced, of which Time-to-Collision (TTC) is the most well-known and commonly applied indicator. TTC first was defined as the time remaining prior to a collision if the path and speed of vehicles are maintained as a constant. Various modifications have been applied to this indicator to increase the efficiency of TTC. However, there is still no specific platform that shows how all of these modifications can be applied simultaneously. Thus, this paper first reviews the modifications that have been made to TTC; subsequently, a comprehensive framework is proposed for the application of this indicator for different types of conflicts, while considering all previous modifications. The framework is then applied for microscopic traffic data collected in Tehran. Results indicate that there is a great difference between the results of different versions of TTC for collision probability prediction. However, the proposed procedure might be beneficial in increasing the precision and accuracy of detecting dangerous encounters. [ABSTRACT FROM AUTHOR]

Does the brightness of an approaching vehicle affect a pedestrian's crossing decision? Thirty participants indicated their street-crossing intentions when facing approaching light or dark vehicles. The experiment was conducted in a real daylight environment and, additionally, in a corresponding virtual one. A real road with actual cars provides high face validity, while a virtual environment ensures the scenario's precise reproducibility and repeatability for each participant. In both settings, participants judged dark vehicles to be a more imminent threat—either closer or moving faster—when compared with light ones. Secondary results showed that participants accepted a significantly shorter time-to-contact when crossing the street in the virtual setting than on the real road. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine whether training with stroboscopic viewing could improve time-to-collision (TTC) judgments, which have importance in real-world tasks such as driving.Background: Prior research demonstrated that training with stroboscopic vision can improve motion coherence thresholds, improve anticipatory timing performance for laterally moving objects, and can protect against performance degradation over time.Method: Participants viewed computer simulations of an object that moved and then disappeared. In two separate experiments, the object approached the observer or moved laterally toward a target, representing different optical flow patterns. Participants judged TTC by pressing a button when they thought the object would hit them (approach), or the target (lateral). Performance was measured during four sessions-pretest, intervention, immediately after intervention, and 10 min after intervention.Results: Both stroboscopic training and repeated practice improved performance over time for approach motion (decrease in constant error) and stroboscopic training protected against performance degradation for lateral motion (no decrement in variable error), but only when TTC was 3.0 s. There was no difference between training and repeated practice.Conclusion: Under certain conditions, stroboscopic training may improve TTC judgments. However, effects of stroboscopic training depend on the nature of the optical flow pattern.Application: It is important to determine the conditions under which training can improve TTC judgments which have importance in real-world tasks such as driving. If individuals can be trained to judge TTC more accurately, they may benefit from driver training programs. [ABSTRACT FROM AUTHOR]

Previous studies have shown that time to arrival estimates (TTA) as well as accepted gap size are influenced by vehicle size. Drivers tend to choose smaller gaps and provide longer TTA estimates when confronted with smaller vehicles compared to larger ones. Object size alone, as well as the potential threat ascribed to the approaching vehicle, have been suggested as being the factors that underlie this bias. To assess the merit of these potential explanations, we used different types of two-wheelers (bicycle, e-bike, scooter) of roughly the same size, but substantial differences in the potential threat they pose. Participants saw videos of approaching two-wheelers and were required to either indicate the smallest acceptable gap, or to estimate TTA (two blocks). Participants accepted smaller gaps for the two bicycle types than for the scooter. Likewise, TTA estimates for the two bicycles were longer than for the scooter. These results indicate that certain characteristics of the vehicle unrelated to physical size, such as the potential threat the vehicle poses, play a role in the assessment of a vehicle's approach. This implies that other road users might select potentially risky gaps when turning in front of bicycles and e-bikes in traffic. [ABSTRACT FROM AUTHOR]

Predicting human trajectories is a challenging task due to the complexity of pedestrian behavior, which is influenced by external factors such as the scene's topology and interactions with other pedestrians. A special challenge arises from the dependence of the behavior on the density of the scene. In the literature, deep learning algorithms show the best performance in predicting pedestrian trajectories, but so far just for situations with low densities. In this study, we aim to investigate the suitability of these algorithms for high-density scenarios by evaluating them on different error metrics and comparing their accuracy to that of knowledge-based models that have been used since long time in the literature. The findings indicate that deep learning algorithms provide improved trajectory prediction accuracy in the distance metrics for all tested densities. Nevertheless, we observe a significant number of collisions in the predictions, especially in high-density scenarios. This issue arises partly due to the absence of a collision avoidance mechanism within the algorithms and partly because the distance-based collision metric is inadequate for dense situations. To address these limitations, we propose the introduction of a novel continuous collision metric based on pedestrians' time-to-collision. Subsequently, we outline how this metric can be utilized to enhance the training of the algorithms. • Empirical comparison of different approaches for predicting pedestrian trajectories. • The accuracy is tested across a range of different pedestrian densities. • Comparing on three accuracy criteria: an Euclidean, a distance, and a TTC metric. • Improving the predictions by incorporating TTC into the loss function. [ABSTRACT FROM AUTHOR]

This report documents the findings of the study “Vehicle/Driver Monitoring for Enhanced Safety of Transit Buses” conducted as part of the PATH project TO5400. The goal of study TO5400 is todesign and implementation of a reliable vehicle/driver monitoring system with aim to enhance driving safety of transit buses.The main objectives of the project are: (1) to identify a simply model that best describes the driving patterns of human drivers, and (2) to develop an algorithm which can generate warning messages when there is a provision of danger.In this study, it is assumed that the driver sets the angle of the steering wheel in response to the lateral deviation of the vehicle from the center line of the road and/or the road curvature ahead of the vehicle which he/she infers from the visual perception. It is also assumed that the dynamic relationship between the lateral deviation and the road curvature (i.e. the inputs to the driver) and the steering angle (i.e. the output from the driver) may be represented by a linear model with Auto Regressive Moving Average with eXogeneous (ARMAX) structure. The order of the ARMAX model is determined by off-line analysis of experimental data collected from actual driving tests. Once the order of the model is determined, the parameters of the model can be estimated by processing the input and output data in real time during actual driving.The on-line data processing and prediction can also estimate three critical parameters for driving safety: the driver’s response time (tr), the time-to-collision (TTC) and the time-to-lane-crossing (TLC) TLC. A timely warning message scheme, which incorporates all the three parameters, is designed to determine whether an alarm should be issued.

The human egocentric perception of approaching objects and the related perceptual processes have been of interest to researchers for several decades. This article gives a literature review on numerous studies that investigated the phenomenon when an object approaches an observer (or the other way around) with the goal to single out factors that influence the perceptual process. A taxonomy of metrics is followed by a breakdown of different experimental measurement methods. Thereinafter, potential factors affecting the judgment of approaching objects are compiled and debated while divided into human factors (e.g., gender, age, and driving experience), compositional factors (e.g., approaching velocity, spatial distance, and observation time), and technical factors (e.g., field of view, stereoscopy, and display contrast). Experimental findings are collated, juxtaposed, and critically discussed. With virtual reality devices having taken a tremendous developmental leap forward in the past few years, they have been able to gain ground in experimental research. Therefore, special attention in this article is also given to the perception of approaching objects in virtual environments and put in contrast to the perception in reality. [ABSTRACT FROM AUTHOR]

• Perceptual judgments in addition to response times were affected by automation. • Time-to-contact judgments were more accurate during automated than manual driving. • Drivers used cognitive resources for the driving task rather than a secondary task. We compared time-to-collision (TTC) judgments between automated and manual driving to determine whether automation affected only responses (i.e., braking) or also affected visual perception (i.e., TTC estimation). Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information (e.g., optic flow) relevant for judgments of TTC. With a driving simulator, participants completed drives using manual or automated driving and responded to a rapidly decelerating vehicle using either the brake pedal or a button on the steering wheel (TTC judgment). They also completed a cognitive secondary task during half of the drives. Results suggest that automation can affect perceptual judgments (e.g., TTC estimation) in addition to driving responses (e.g., braking). TTC judgments were more accurate, and brake reaction times were faster, during automated driving than manual driving. This occurred even while performing a cognitively demanding secondary task, suggesting that participants used resources freed by automation to process visual information relevant to TTC judgments rather than complete non-driving tasks. TTC judgments were more accurate during automated driving than manual driving, presumably because automation freed up cognitive resources, allowing participants to assign those resources to processing of visual information (e.g., optic flow) relevant to judgments about collisions. To realize the safety benefit suggested by the results, automated systems should be designed so that cognitive resources freed by automation are assigned to information relevant to the driving task and not to non-driving tasks. [ABSTRACT FROM AUTHOR]

This article examines the optimum values of postencroachment time (PET) and time-to-collision (TTC) parameters that would define a pedestrian-to-vehicle conflict at signalized intersections using a simulation model (VISSIM) and a surrogate safety assessment model (SSAM). A total of 42 video hours were recorded at seven signalized intersections for field data collection. The observed conflicts from the field were used to calibrate VISSIM and replicate the conflicts. The calibrated and validated VISSIM model generated the pedestrian-vehicle conflicts from SSAM software using the vehicle trajectory data in VISSIM. The mean absolute percent error (MAPE) was used to determine the optimum TTC and PET thresholds for pedestrian-vehicle conflicts and linear regression analysis was used to study the correlation between the observed and simulated conflicts at the established thresholds. The results of the regression analysis indicated the highest correlation between the simulated and observed conflicts when the TTC parameter was set at 2.7 and the PET was set at 8. It was also found that the VISSIM model might underestimate the number of pedestrian-vehicle conflicts at specific intersections that would involve illegal pedestrian behavior such as jay walking or pedestrian signal violation that would occur in the real world. [ABSTRACT FROM AUTHOR]

Rear-end collisions often cause serious traffic accidents. Conventionally, in intelligent transportation systems (ITS), radar collision warning methods are highly accurate in determining the inter-vehicle distance via detecting the rear-end of a vehicle; however, in poor weather conditions such as fog, rain, or snow, the accuracy is significantly affected. In recent years, the advent of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication systems has introduced new methods for solving the rear-end collision problem. Nevertheless, there is still much left for improvement. For instance, weather conditions have an impact on human-related factors such as response time. To address the issue of collision detection under low visibility conditions, we propose a Visibility-based CollisionWarning System (ViCoWS) design that includes four models for prediction horizon estimation, velocity prediction, headway distance prediction, and rear-end collision warning. Based on the history of velocity data, future velocity volumes are predicted. Then, the prediction horizon (number of future time slots to consider) is estimated corresponding to different weather conditions. ViCoWs can respond in real-time to weather conditions with correct collision avoidance warnings. Experiment results show that the mean absolute percentage error of our velocity prediction model is less than 11%. For non-congested traffic under heavy fog (very low visibility of 120 m), ViCoWS warns a driver by as much as 4.5 s prior to a possible future collision. If the fog is medium with a low visibility of 160 m, ViCoWs can give warnings by about 2.1 s prior to a possible future collision. In contrast, the Forward Collision Probability Index (FCPI) method gives warnings by only about 0.6 s before a future collision. For congested traffic under low visibility conditions, ViCoWS can warn a driver by about 1.9 s prior to a possible future collision. In this case, the FCPI method gives 1.2 s for the driver to react before collision. [ABSTRACT FROM AUTHOR]

Analyzing a crash using driving recorder data makes it possible to objectively examine factors contributing to the occurrence of the crash. In this study, car-to-cyclist crashes and near crashes recorded on cars equipped with advanced driving recorders were compared with each other in order to examine the factors that differentiate near crashes from crashes, as well as identify the causes of the crashes. Focusing on cases where the car and cyclist approached each other perpendicularly, the differences in the car’s and cyclist’s parameters such as velocity, distance and avoidance behavior were analyzed. The results show that car-to-cyclist crashes would not be avoidable when the car approaching the cyclist enters an area where the average deceleration required to stop the car is more than 0.45 G (4.4 m/s 2 ). In order for this situation to occur, there are two types of cyclist crash scenarios. In the first scenario, the delay in the drivers’ reaction in activating the brakes is the main factor responsible for the crash. In this scenario, time-to-collision when the cyclist first appears in the video is more than 2.0 s. In the second scenario, the sudden appearance of a cyclist from behind an obstacle on the street is the factor responsible for the crash. In this case, the time-to-collision is less than 1.2 s, and the crash cannot be avoided even if the driver exhibited avoidance maneuvers. [ABSTRACT FROM AUTHOR]

Connected vehicles (CV) technology has recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. One of the biggest issues facing CVs popularization associates it with the market penetration rate (MPR). The full market penetration of CVs might not be accomplished recently. Therefore, traffic flow will likely be composed of a mixture of conventional vehicles and CVs. In this context, the study of CV MPR is worthwhile in the CV transition period. The overarching goal of this study was to evaluate longitudinal safety of CV platoons by comparing the implementation of managed-lane CV platoons and all lanes CV platoons (with same MPR) over non-CV scenario. This study applied the CV concept on a congested expressway (SR408) in Florida to improve traffic safety. The Intelligent Driver Model (IDM) along with the platooning concept were used to regulate the driving behavior of CV platoons with an assumption that the CVs would follow this behavior in real-world. A high-level control algorithm of CVs in a managed-lane was proposed in order to form platoons with three joining strategies: rear join, front join, and cut-in joint. Five surrogate safety measures, standard deviation of speed, time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), and sideswipe crash risk (SSCR) were utilized as indicators for safety evaluation. The results showed that both CV approaches (i.e., managed-lane CV platoons, and all lanes CV platoons) significantly improved the longitudinal safety in the studied expressway compared to the non-CV scenario. In terms of surrogate safety measures, the managed-lane CV platoons significantly outperformed all lanes CV platoons with the same MPR. Different time-to-collision (TTC) thresholds were also tested and showed similar results on traffic safety. Results of this study provide useful insight for the management of CV MPR as managed-lane CV platoons. [ABSTRACT FROM AUTHOR]