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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.)

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., (© 2024 The Authors. PsyCh Journal published by Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.)

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 (n ADHD  = 20, n ASD  = 20, n TD  = 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., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The passing maneuver (PM) on two-lane rural highways is a very complicated driving task because it is associated with the risk of a head-on collision. It can be more risky for multiple PMs where a single vehicle passes at least two other ones. Using the time-to-collision (TTC) indicator, this paper addressed the risk of multiple PMs and developed a logistic regression model to predict the occurrence probability of a serious head-on conflict defined with a TTC of <3 sec-threshold. The effective accepted gap (EAG), passing duration, number of passed vehicles, and the end position of maneuvers were the explanatory variables and the data used to estimate the model were collected from 319 PMs using a drone at seven passing zones in Iran. Results indicated that 85% of the passing vehicles were safe in a single PM with EAGs greater than 22.4 sec for all types of passed vehicles. This safe EAG increased by up to 30.9 sec for vehicles involved in multiple PMs with two passed vehicles and 52 sec for those with three or more. Heavy vehicles required 3.1 sec more EAG than passenger cars to conduct a single PM and maneuvers ended in no-passing zones were more risky than those ending in a passing zone., (Copyright © 2021. Published by Elsevier Ltd.)

The time-to-collision (TTC) index and its extended variants have been widely utilized to assess rear-end collision risks, but the characteristics of the time-series data have not been fully explored, especially for the transition from safe to risky conditions. This study proposes a novel approach in rear-end collision risk analysis based on the concept of transition durations. The vehicle trajectory data were extracted and the TTC index was used to identify risky and safe conditions. Three important transition durations are defined and their rationalities for evaluating rear-end collision risks are examined by developing random-parameters accelerated failure time (AFT) survival models. Furthermore, a typical case from real trajectory data is taken to discuss the limitations of using TTC and its variants, and the advantage of the proposed transition durations. The results of random-parameters AFT models reveal contributing factors affecting the length of three durations and demonstrate the rationality of transition durations in rear-end collision risks analysis. It is indicated that the proposed method outperforms TTC and its variants in evaluating rear-end collision risks, because it could not only provide the information of time point but also the variation of time-series data., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV's market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk. The analysis showed a remarkable improvement in TTC as a result of the notably better speed adjustments of the following vehicle (i.e., lower differences in speeds between the lead and following vehicles) in the second scenario. Furthermore, human-driven vehicles were found to consume less fuel and produce fewer emissions on average when following an AV., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (Copyright: © 2020 University of Economics and Human Sciences in Warsaw.)

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., (Copyright © 2020 National Safety Council and Elsevier Ltd. All rights reserved.)

Observers are able to make generally accurate judgments of the time-to-collision (TTC) of approaching stimuli. Traditional theories have emphasized the role of optical cues about the expansion of the retinal image in this ability. Recent work, however, has further emphasized the role of semantic information about the object. Here we investigate the role of semantic information in TTC judgments by presenting a range of real-world objects, which varied widely in size, weight, and hardness. Our results show that the physical characteristics of looming stimuli predict observers' TTC estimations. Bigger, heavier, and harder objects were underestimated more, relative to smaller, lighter, and softer objects. As expected, actual TTC and stimulus size were also significant predictors of TTC judgments. In estimating the arrival time of looming stimuli, observers automatically take into account several characteristics of the stimuli, even though these characteristics are completely task irrelevant. This suggests that semantic properties of seen objects and the consequences of their impact on the observer's body are processed automatically., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Time-to-collision (TTC) index has been extensively utilized to evaluate rear-end collision risks, but few studies have focused on the special transition process that vehicles change from a safe to a dangerous situation. This study conducts an in-depth analysis of the transition condition of rear-end collisions. Realistic vehicle trajectory data were extracted from the Federal Highway Administration's Next Generation Simulation (NGSIM) datasets. The TTC index was utilized to pinpoint dangerous and transition conditions. A total of 13 types of transition conditions were categorized and a novel indicator, the derivative of TTC (TTCD), is proposed to evaluate changing rate of TTCs. Three types of TTCDs, corresponding to different time point or interval, were further analyzed based on developed regression models. The results indicate that: (1) although theoretically there are a total of 13 types of transition conditions, three types are dominant in practice; (2) the TTCD(t 0 ) values at transition start points are significantly smaller than the TTCD(t 1 ) at end points and the average TTCD(t 0 ,t 1 ), which indicates the quickest change of TTC values, while the TTCD(t 1 ) has the slowest changes of TTC values; and (3) the following vehicle's speed and acceleration rate, and speed difference and acceleration difference between two vehicles have significant effects on TTCDs. The influences are more remarkable of TTCD(t 0 ) than those of TTCD(t 1 ), and the TTCD(t 0 ,t 1 ) always shows the average characteristic. Lastly, corresponding countermeasures are discussed based on findings above., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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.

Virtual reality (VR) is a valuable tool for the assessment of human perception and behavior in a risk-free environment. Investigators should, however, ensure that the used virtual environment is validated in accordance with the experiment's intended research question since behavior in virtual environments has been shown to differ to behavior in real environments. This article presents the street crossing decisions of 30 participants who were facing an approaching vehicle and had to decide at what moment it was no longer safe to cross, applying the step-back method. The participants executed the task in a real environment and also within a highly immersive VR setup involving a head-mounted display (HMD). The results indicate significant differences between the two settings regarding the participants' behaviors. The time-to-contact of approaching vehicles was significantly lower for crossing decisions in the virtual environment than for crossing decisions in the real one. Additionally, it was demonstrated that participants based their crossing decisions in the real environment on the temporal distance of the approaching vehicle (i.e., time-to-contact), whereas the crossing decisions in the virtual environment seemed to depend on the vehicle's spatial distance, neglecting the vehicle's velocity. Furthermore, a deeper analysis suggests that crossing decisions were not affected by factors such as the participant's gender or the order in which they faced the real and the virtual environment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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.

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

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 Collision Warning 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.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The aim of this study was to describe the position of pedestrians and pedal cyclists relative to the striking vehicle in the 3 s before impact. This information is essential for the development of effective autonomous emergency braking systems and relevant test conditions for consumer ratings. The UK RAIDS-OTS study provided 175 pedestrian and 127 pedal-cycle cases based on in-depth, at-scene investigations of a representative sample of accidents in 2000-2010. Pedal cyclists were scattered laterally more widely than pedestrians (90% of cyclists within around ±80° compared to ±20° for pedestrians), however their distance from the striking vehicle in the seconds before impact was no greater (90% of cyclists within 42 m at 3 s compared to 50 m for pedestrians). This data is consistent with a greater involvement of slow moving vehicles in cycle accidents. The implication of the results is that AEB systems for cyclists require almost complete 180° side-to-side vision but do not need a longer distance range than for pedestrians., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Objective: Intersection movement assist (IMA) has been recognized as one of the prominent countermeasures to reduce angle crashes at intersections, which constitute 22% of total crashes in the United States. Utilizing vehicle-based sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communications, IMA offers extended vision to provide early warning for an imminent crash. However, most of IMA-related research implements their methods and strategies only in simulations, test tracks, or driving simulator studies that have quite a few assumptions and limitations and hence the effectiveness evaluations reported may not be transferable or comparable., Methods: This study seeks to develop a generalized evaluation scheme that can be used not only to assess the effectiveness of IMA on improving traffic safety at intersections but to facilitate comparisons across similar studies. The proposed evaluation scheme utilizes the concepts of traffic conflict in terms of time-to-collision (TTC) as a crash surrogate. This approach avoids the issue of having insufficient crash frequency data for system evaluation. To measure the effectiveness of IMA on reducing traffic conflicts, a relative risk is calculated for comparing the risk of with/without using the IMA. As a proof-of-concept study, this study applied the proposed evaluation scheme and reported the effectiveness of IMA on improving traffic safety in a field operation test (FOT). Seven test scenarios were conducted at 4 intersections, and a total of 40 participants were recruited to use the IMA for 6 months., Results: It was estimated that IMA users have 26% fewer conflicts with TTC less than 5 s and have 15% fewer conflicts with TTC less than 4 s. However, the results vary across different sites and different definitions of conflicts in terms of TTC., Conclusions: Overall, IMA is promising to effectively reduce angle crashes related to sight obstruction and has potential to reduce not only crash frequency but crash severity.

Time-to-collision (TTC) underestimation has been interpreted as an adaptive response that allows observers to have more time to engage in a defensive behaviour. This bias seems, therefore, strongly linked to action preparation. There is evidence that the observer's physical fitness modulates the underestimation effect so that people who need more time to react (i.e. those with less physical fitness) show a stronger underestimation effect. Here we investigated whether this bias is influenced by the momentary action capability of the observers. In the first experiment, participants estimated the time-to-collision of threatening or non-threatening stimuli while being mildly immobilized (with a chin rest) or while standing freely. Having reduced the possibility of movement led participants to show more underestimation of the approaching stimuli. However, this effect was not stronger for threatening relative to non-threatening stimuli. The effect of the action capability found in the first experiment could be interpreted as an expansion of peripersonal space (PPS). In the second experiment, we thus investigated the generality of this effect using an established paradigm to measure the size of peripersonal space. Participants bisected lines from different distances while in the chin rest or standing freely. The results replicated the classic left-to-right gradient in lateral spatial attention with increasing viewing distance, but no effect of immobilization was found. The manipulation of the momentary action capability of the observers influenced the participants' performance in the TTC task but not in the line bisection task. These results are discussed in relation to the different functions of PPS.

In this study, results from two different hazard perception tests are presented: the first one is a classic hazard-perception test in which participants must respond - while watching real traffic video scenes - by pressing the space bar in a keyboard when they think there is a collision risk between the camera car and the vehicle ahead. In the second task we use fragments of the same scenes but in this case they are adapted to a signal detection task - a 'yes'/'no' task. Here, participants - most of them, University students - must respond, when the fragment of the video scene ends, whether they think the collision risk had started yet or not. While in the first task we have a latency measure (the time necessary for the driver to respond to a hazard), in the second task we obtain two separate measures of sensitivity and criterion. Sensitivity is the driver's ability to discriminate in a proper way the presence vs. absence of the signal (hazard) while the criterion is the response bias a driver sets to consider that there is a hazard or not. His/her criterion could be more conservative - the participant demands many cues to respond that the signal is present, neutral or even liberal - the participant will respond that the signal is present with very few cues. The aim of the study is to find out if our latency measure is associated with a different sensitivity and/or criterion. The results of the present study show that drivers who had greater latencies and drivers who had very low latencies yield a very similar sensitivity mean value. Nevertheless, there was a significant difference between these two groups of drivers in criterion: those drivers who had greater latencies in the first task were also more conservative in the second task. That is, the latter responded less frequently that there was danger in the sequences. We interpret that greater latencies in our first hazard perception test could be due to a stricter or more conservative criterion, rather than a low sensitivity to perceptual information for collision risk. Drivers with a more conservative criterion need more evidences of danger, thus taking longer to respond., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Passing on two-lane rural highways is associated with risks of head-on collision resulting from unsafe completion of passing maneuvers in the opposite traffic lane. In this paper, we explore the use of time-to-collision (TTC) as a surrogate safety measure of the risk associated with passing maneuvers. Logistic regression models to predict the probability to end the passing maneuver with TTC less than 2 or 3s-threshold were developed with the time-gap from initiation of the maneuver to arrival of the opposite vehicle (effective accepted gap), and the passing duration as explanatory variables. The data used for model estimation was collected using stationary tripod-mounted camcorders at 19 passing zones in Uganda. Results showed that passing maneuvers completed with TTC less than 3s are unsafe and often involved sudden speed reduction, flashing headlights, and lateral shift to shoulders. Model sensitivity analysis was conducted for observed passing durations involving passenger cars or short trucks (2-3 axles), and long trucks (4-7 axles) as the passed vehicles for 3s TTC-threshold. Three risk levels were proposed based on the probability to complete passing maneuvers with TTC less than 3s for a range of opposite direction traffic volumes. Applications of the results for safety improvements of two-lane rural highways are also discussed., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objects on a collision course with an observer produce a specific pattern of optical expansion on the retina known as looming, which in theory exactly specifies the time-to-collision (TTC) of approaching objects. It was recently demonstrated that the affective content of looming stimuli influences perceived TTC, with threatening objects judged as approaching sooner than non-threatening objects. Here, the neural mechanisms by which perceived threat modulates spatiotemporal perception were investigated. Participants judged the TTC of threatening (snakes, spiders) or non-threatening (butterflies, rabbits) stimuli, which expanded in size at a rate indicating one of five TTCs. Visual-evoked potentials (VEPs) and oscillatory neural responses measured with electroencephalography were analysed. The arrival time of threatening stimuli was underestimated compared with non-threatening stimuli, though an interaction suggested that this underestimation was not constant across TTCs. Further, both speed of approach and threat modulated both VEPs and oscillatory responses. Speed of approach modulated the N1 parietal and oscillations in the beta band. Threat modulated several VEP components (P1, N1 frontal, N1 occipital, early posterior negativity and late positive potential) and oscillations in the alpha and high gamma band. The results for the high gamma band suggest an interaction between these two factors. Previous evidence suggests that looming stimuli activate sensorimotor areas, even in the absence of an intended action. The current results show that threat disrupts the synchronization over the sensorimotor areas that are likely activated by the presentation of a looming stimulus., (© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

During overtaking maneuvers on two-way highways drivers must temporarily cross into the opposite lane of traffic, and may face oncoming vehicles. To judge when it is safe to overtake, drivers must estimate the time-to-contact (TTC) of the oncoming vehicle. Information about an oncoming vehicle's TTC is available in the optical expansion pattern, but it is below threshold during high-speed overtaking maneuvers, which require a large passing distance. Consequently, we hypothesized that drivers would rely on perceived distance and velocity, and that their overtaking judgments would be influenced by oncoming vehicle size. A driving simulator was used to examine whether overtaking judgments are influenced by the size of an oncoming vehicle, and by whether a driver actively conducts the overtaking maneuver or passively judges whether it is safe to overtake. Oncoming motorcycles resulted in more accepted gaps and false alarms than larger cars or trucks. Results were due to vehicle size independently of vehicle type, and reflected shifts in response bias rather than sensitivity. Drivers may misjudge the distances of motorcycles due to their relatively small sizes, contributing to accidents due to right-of-way violations. Results have implications for traffic safety and the potential role of driver-assistance technologies., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

With regard to impending object-object collisions, observers may use different sources of information to judge time to contact (tC). We introduced changes of the observer's vantage point to test among three sets of hypotheses: (1) Observers may use a distance-divided-by-velocity algorithm or, alternatively, elaborated τ-formulae, all of which give exact tC information; (2) observers may use simple τ-formulae (i.e., formulae of the type: visual angle divided by its own first temporal derivative); (3) observers may capitalize on non-τ variables. Hypotheses (2) and (3) imply specific patterns of errors. We presented animated, impending collisions between a moving object and a stationary pole to naïve observers. The moving object either was a square tile or a small dot of fixed size. Participants viewed these events in a prediction-motion paradigm from different vantage points, covering a full circle around the setting. As accuracy of responses varied sinusoidally with viewing angle, irrespective of the type of object used, we conclude that observers mainly responded to the perspective view of the gap between object and pole, and less to the object's changing visual angle, or τ. Results are discussed with regard to evolutionary demands and issues of generalization., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

In dynamic locomotory contexts, visual cues often trigger adaptive behaviour by the viewer, yet studies investigating how animals determine impending collisions typically employ either stationary viewers or objects. Here, we describe a dynamic situation of visually guided prey pursuit in which both impending prey contact and escape elicit observable adaptive behaviours in the pursuer, a predatory beetle. We investigated which visual cues may independently control opening and closing of the beetle's jaws during chases of prey dummies. Jaw opening and closing typically occur when prey is within the 60° binocular field, but not at specific distances, angular sizes or time-to-collision. We show that a sign change in the expansion rate of the target image precedes jaw opening (16 ms) and closing (35 ms), signalling to the beetle that it is gaining on the target or that the target is getting away. We discuss the 'sloppiness' of such variation in the lag of the behavioural response, especially jaw closing, as an adaptation to uncertainty about target position due to degradation of the target image by motion blur from the fast-running beetle., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

In order to overcome urban space constraints, underground road systems are becoming popular options for cities. Existing literature suggests that accident rates in road tunnels are lower than those in open roads. However, there is a lack of understanding in how the road tunnel environment affects inter-vehicle interactions. In this study, car following data are obtained from traffic video footages of open and tunnel expressways in Singapore. A total of 15,325 car following headways (with car as the follower) are analysed and significant factors affecting headways are found to be speed, and lane. Significant effect of leading vehicle type is only found for tunnel expressway. Headways are generally longer in the tunnel environment. Assessment of collision time measures and safety margins also reveal safer car following behaviour and lower rear-end collision risks in the tunnel expressway. The results are discussed from a behavioural perspective. Overall, the findings show that road tunnels are superior in terms of safety but at reduced traffic capacity ., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Many animals avoid dark, approaching objects seen against a lighter background but show no or weaker reactions to stimuli with inverted contrast. We investigated whether human observers would respond differently to such stimuli in terms of estimated time-to-arrival. We varied luminances of an approaching, light or dark disk and a plain, grey background, and for several conditions, continuously adjusted calibrations so as to keep contrast and/or overall lightness constant. Since no effects were found, we conclude that humans are able to discard luminance and contrast for the task at hand. Generally, however, performance was affected by different, consecutive regimes of feedback: Initially, without feedback, observers responded inconsistently and much too late; they improved after correct feedback, and in a third block of trials with pseudo-random feedback, they responded increasingly early without reverting to the initial level of uncertainty. We discuss our findings with regard to implications for neural mechanisms, put them in the context of evolutionary considerations, and propose continuative animal behavioral studies., (Copyright © 2013 Elsevier Ltd. All rights reserved.)