A binary acceleration signal reduces overestimation in pedestrians' visual time-to-collision estimation for accelerating vehicles

When a pedestrian intends to cross the street, it is essential for safe mobility to correctly estimate the arrival time (time-to-collision, TTC) of an approaching vehicle. However, visual perception of acceleration is rather imprecise. Previous studies consistently showed that humans (mostly) disregard acceleration, but judge the TTC for an object as if it were traveling at constant speed (first-order estimation), which is associated with overestimated TTCs for positively accelerating objects. In a traffic context, such TTC overestimation could motivate pedestrians to cross in front of an approaching vehicle, although the time remaining is not sufficiently long. Can a simple acceleration signal help improve visual TTC estimation for accelerating objects? The present study investigated whether a signal that only indicates whether a vehicle is accelerating or not can remove the first-order pattern of overestimated TTCs. In a virtual reality simulation, 26 participants estimated the TTC of vehicles that approached with constant velocity or accelerated, from the perspective of a pedestrian at the curb. In half of the experimental blocks, a light band on the windshield illuminated whenever the vehicle accelerated but remained deactivated when the vehicle travelled at a constant speed. In the other blocks, the light band never illuminated, regardless of whether or not the vehicle accelerated. Participants were informed about the light band function in each block. Without acceleration signal, the estimated TTCs for the accelerating vehicles were consistent with an erroneous first-order approximation. In blocks with acceleration signal, participants substantially changed their estimation strategy, so that TTC overestimations for accelerating vehicles were reduced. Our data suggest that a binary acceleration signal helps pedestrians to effectively reduce the TTC overestimation for accelerating vehicles and could therefore increase pedestrian safety.

The effect of background information and motion speed on the performance of TTC estimation

BACKGROUND

In previous studies, most research on motion perception have been conducted under background-free condition when the stimulus moved in a plane parallel to the observer. In real-life situations, people's perception of the motion state of objects is usually done under different visual noise. Based on the occlusion paradigm, this study aimed to investigate whether different background information and motion speed affect the trend and accuracy of time-to-collision (TTC) estimation when stimuli move in a plane parallel to the observer.

METHODS

Thirty five college students (mean age = 20.94, SD = 2.95, range = 18-28 years) participated in experiment 1, and used a 2 (background orientation: horizontal, vertical) × 3 (motion speed: slow, medium, fast) design to explore the effect of different line segment orientations and motion speed on TTC estimation performance; 36 college students (mean age = 20.81, SD = 2.82, range = 18-28 years) participated in experiment 2, and used a 2 (background dimension: two-dimensional background, three-dimensional background) × 3 (motion speed: slow, medium, fast) design to explore the effect of different background dimensions and motion speed on the performance of TTC estimation. The data were analyzed using SPSS 25.0.

RESULTS

The results revealed that: (1) The TTC was underestimated for the slow speed condition and overestimated for the medium and fast speed conditions. (2) The highest accuracy of TTC estimation was obtained for the fast condition. (3) The TTC were overestimated for the vertical background condition and underestimated for the horizontal background condition. (4) Compared to the two-dimensional background, the TTC was overestimated in the three-dimensional background.

CONCLUSIONS

Object motion speed affected the TTC estimation performance, and different background information affected the TTC estimation performance when the object moved in a plane parallel to the observer. Meanwhile, the impact of background orientation and motion speed showed significant interactions.

Camera-Monitor Systems as An Opportunity to Compensate for Perceptual Errors in Time-to-Contact Estimations

For the safety of road traffic, it is crucial to accurately estimate the time it will take for a moving object to reach a specific location (time-to-contact estimation, TTC). Observers make more or less accurate TTC estimates of objects of average size that are moving at constant speeds. However, they make perceptual errors when judging objects which accelerate or which are unusually large or small. In the former case, for instance, when asked to extrapolate the motion of an accelerating object, observers tend to assume that the object continues to move with the speed it had before it went out of sight. In the latter case, the TTC of large objects is underestimated, whereas the TTC of small objects is overestimated, as if physical size is confounded with retinal size (the size-arrival effect). In normal viewing, these perceptual errors cannot be helped, but camera-monitor systems offer the unique opportunity to exploit the size-arrival effect to cancel out errors induced by the failure to respond to acceleration. To explore whether such error cancellation can work in principle, we conducted two experiments using a prediction-motion paradigm in which the size of the approaching vehicle was manipulated. The results demonstrate that altering the vehicle's size had the expected influence on the TTC estimation. This finding has practical implications for the implementation of camera-monitor systems.

Overestimated time-to-collision for quiet vehicles: Evidence from a study using a novel audiovisual virtual-reality system for traffic scenarios

To avoid collision, pedestrians intending to cross a road need to estimate the time-to-collision (TTC) of an approaching vehicle. Here, we present a novel interactive audiovisual virtual-reality system for investigating how the acoustic characteristics (loudness and engine type) of vehicles influence the TTC estimation. Using acoustic recordings of real vehicles as source signals, the dynamic spatial sound fields corresponding to a vehicle approaching in an urban setting are generated based on physical modeling of the sound propagation between vehicle and pedestrian and are presented via sound field synthesis. We studied TTC estimation for vehicles with internal combustion engine and for loudness-matched electric vehicles. The vehicle sound levels were varied by 10 dB, independently of the speed, presented TTC, and vehicle type. In an auditory-only condition, the cars were not visible, and lower loudness of the cars resulted in considerably longer TTC estimates. Importantly, the loudness of the cars also had a significant effect in the same direction on the TTC estimates in an audiovisual condition, where the cars were additionally visually presented via interactive virtual-reality simulations. Thus, pedestrians use auditory information when estimating TTC, even when full visual information is available. At equal loudness, the TTC judgments for electric and conventional vehicles were virtually identical, indicating that loudness has a stronger effect than spectral differences. Because TTC overestimations can result in risky road crossing decisions, the results imply that vehicle loudness should be considered as an important factor in pedestrian safety.

Toward a Holistic Communication Approach to an Automated Vehicle's Communication With Pedestrians: Combining Vehicle Kinematics With External Human-Machine Interfaces for Differently Sized Automated Vehicles

Future automated vehicles (AVs) of different sizes will share the same space with other road users, e. g., pedestrians. For a safe interaction, successful communication needs to be ensured, in particular, with vulnerable road users, such as pedestrians. Two possible communication means exist for AVs: vehicle kinematics for implicit communication and external human-machine interfaces (eHMIs) for explicit communication. However, the exact interplay is not sufficiently studied yet for pedestrians' interactions with AVs. Additionally, very few other studies focused on the interplay of vehicle kinematics and eHMI for pedestrians' interaction with differently sized AVs, although the precise coordination is decisive to support the communication with pedestrians. Therefore, this study focused on how the interplay of vehicle kinematics and eHMI affects pedestrians' willingness to cross, trust and perceived safety for the interaction with two differently sized AVs (smaller AV vs. larger AV). In this experimental online study (N = 149), the participants interacted with the AVs in a shared space. Both AVs were equipped with a 360° LED light-band eHMI attached to the outer vehicle body. Three eHMI statuses (no eHMI, static eHMI, and dynamic eHMI) were displayed. The vehicle kinematics were varied at two levels (non-yielding vs. yielding). Moreover, “non-matching” conditions were included for both AVs in which the dynamic eHMI falsely communicated a yielding intent although the vehicle did not yield. Overall, results showed that pedestrians' willingness to cross was significantly higher for the smaller AV compared to the larger AV. Regarding the interplay of vehicle kinematics and eHMI, results indicated that a dynamic eHMI increased pedestrians' perceived safety when the vehicle yielded. When the vehicle did not yield, pedestrians' perceived safety still increased for the dynamic eHMI compared to the static eHMI and no eHMI. The findings of this study demonstrated possible negative effects of eHMIs when they did not match the vehicle kinematics. Further implications for a holistic communication strategy for differently sized AVs will be discussed.

Auditory Information Improves Time-to-collision Estimation for Accelerating Vehicles

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.

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.

One Solution Fits All? Evaluating Different Communication Strategies of a Light-based External Human-Machine Interface for Differently Sized Automated Vehicles from a Pedestrian's Perspective

Differently sized automated vehicles (AVs) will enter the roads of tomorrow and will interact with other road users. Pedestrians as vulnerable road users heavily rely on the communication with other road users, especially for the interaction with larger vehicles, as miscommunication pose a high risk. Therefore, AVs need to provide communication abilities to safely interact with pedestrians. This study's focus was on the explicit communication which is highly relevant in low-speed and low-distance traffic scenarios to clarify misunderstandings before they result in accidents. External human-machine interfaces (eHMIs) placed on the outside of AVs can be used as a communication tool to explicitly inform the surrounding traffic environment. Although research manifested effects of vehicle size on pedestrians' perceived safety and crossing behavior, little research about the eHMI design for differently sized AVs exists. This experimental online study (N = 155) aimed at investigating the application of a light-based eHMI on two differently sized AVs (car, bus) by focusing on the overall goal of ensuring traffic safety in future traffic. The light-based eHMI showed different communication strategies, i.e., a static eHMI and three dynamic eHMIs. The results revealed that an automated car was perceived as safer and affectively rated as more positive compared to an automated bus. Nevertheless, no significant differences were found between the two AVs in terms of the eHMI communication. A dynamic eHMI was perceived as safer and evaluated affectively as more positive compared to a static eHMI or no eHMI for both AVs. In conclusion, the use of a light-based eHMI had a positive effect on pedestrians' interaction with an automated car and an automated bus and, therefore, could contribute to the overall traffic safety in this study. Implications for the design of eHMIs for differently sized AVs were discussed.

The role of eye movements in perceiving vehicle speed and time-to-arrival at the roadside

To avoid collisions, pedestrians depend on their ability to perceive and interpret the visual motion of other road users. Eye movements influence motion perception, yet pedestrians' gaze behavior has been little investigated. In the present study, we ask whether observers sample visual information differently when making two types of judgements based on the same virtual road-crossing scenario and to which extent spontaneous gaze behavior affects those judgements. Participants performed in succession a speed and a time-to-arrival two-interval discrimination task on the same simple traffic scenario-a car approaching at a constant speed (varying from 10 to 90 km/h) on a single-lane road. On average, observers were able to discriminate vehicle speeds of around 18 km/h and times-to-arrival of 0.7 s. In both tasks, observers placed their gaze closely towards the center of the vehicle's front plane while pursuing the vehicle. Other areas of the visual scene were sampled infrequently. No differences were found in the average gaze behavior between the two tasks and a pattern classifier (Support Vector Machine), trained on trial-level gaze patterns, failed to reliably classify the task from the spontaneous eye movements it elicited. Saccadic gaze behavior could predict time-to-arrival discrimination performance, demonstrating the relevance of gaze behavior for perceptual sensitivity in road-crossing.

The influence of time structure on prediction motion in visual and auditory modalities

Usually people can estimate the correct position of a moving object even when it temporarily moves behind an occlusion. Studies have been performed on this type of occluded motion with prediction motion (PM) tasks in the laboratory. Previous publications have emphasized that people could use mental imagery or apply an oculomotor system to estimate the arrival of a moving stimulus at the target place. Nevertheless, these two ways cannot account for the performance difference under a different set of conditions. Our study tested the role of time structure in a time-to-collision (TTC) task using visual and auditory modalities. In the visual condition, the moving red bar travelled from left to right and was invisible during the entire course but flashed at the initial and the occluded points. The auditory condition and visual condition were alike, except that the flashes in the visual condition were changed to clicks at the initial and the occluded points. The results illustrated that participants' performance was better in the equal time structure condition. The comparison between the two sense modalities demonstrated a similar tendency, which suggested there could be common cognitive processes between visual and auditory modalities when participants took advantage of temporal cues to judge TTC.

Does the Size-Arrival Effect Occur With an Active Collision-Avoidance Task in an Immersive 3D Virtual Reality Environment?

OBJECTIVE

Determine whether the size-arrival effect (SAE) occurs with immersive, 3D visual experiences and active collision-avoidance responses.

BACKGROUND

When a small near object and a large far object approach the observer at the same speeds, the large object appears to arrive before the small object, known as the size-arrival effect (SAE), which may contribute to crashes between motorcycles and cars. Prior studies of the SAE were limited because they used two dimensional displays and asked participants to make passive judgments.

METHOD

Participants viewed approaching objects using a virtual reality (VR) headset. In an active task, participants ducked before the object hit them. In a passive prediction-motion (PM) judgment, the approaching object disappeared, and participants pressed a button when they thought the object would hit them. In a passive relative TTC judgment, participants reported which of two approaching objects would reach them first.

RESULTS

The SAE occurred with the PM and relative TTC tasks but not with the ducking task. The SAE can occur in immersive 3D environments but is limited by the nature of the task and display.

APPLICATION

Certain traffic situations may be more prone to the SAE and have higher risk for collisions. For example, in left-turn scenarios (e.g., see Levulis, 2018), drivers make passive judgments when oncoming vehicles are far and optical expansion is slow, and binocular disparity putatively is ineffective. Collision-avoidance warning systems may be needed more in such scenarios than when vehicles are near and drivers' judgments of TTC may be more accurate (DeLucia, 2008).

Improving motorcycle motion perception by using innovative motorcycle headlight configurations: Evidence from simulator and test-track experiments

Many motorcycle accidents occur at intersections and are caused by other vehicle drivers who misperceive the speed and time-to-arrival of an approaching motorcycle. The two experiments reported here tested different motorcycle headlight configurations likely to counteract this perceptual failure. In the first experiment, conducted on a driving simulator, car drivers turned left in front of cars and motorcycles approaching an intersection under nighttime lighting conditions. The motorcycles were equipped with either a standard white central light, or one of three vertical configurations of white and yellow lights. The results showed that the standard configuration led to significantly more unsafe accepted gaps than the vertical configurations. In the second experiment, conducted on a test track using a similar task, the most promising motorcycle headlight configuration, i.e., the vertical yellow-white light arrangement (one central white light, plus one yellow light on the helmet and two yellow lights on the fork) was evaluated and compared to a standard configuration and a car. The vertical yellow-white headlight configuration again provided significant safety benefits as compared to the standard configuration. These findings demonstrate that motorcycle safety can be improved by headlight ergonomics that accentuate the vertical dimension of motorcycles. They also suggest that the driving simulator is a valid tool for conducting research on motorcycle headlight design.

The Ups and Downs of Camera-Monitor Systems: The Effect of Camera Position on Rearward Distance Perception

OBJECTIVE

This study investigates the effects of different positions of side-mounted rear-view cameras on distance estimation of drivers.

BACKGROUND

Camera-monitor systems bring advantages as compared to conventional rear-view mirrors, such as improved aerodynamics and enlarged field-of-view. Applied research has mainly focused on the comparison between cameras and mirrors or on positioning of in-vehicle monitors. However, the positioning of the exterior camera awaits investigation given that the perspective of the observer at does affect depth perception at large.

METHOD

In two experiments, a total of 50 students estimated metric distances to static vehicles presented in realistic or 3D-rendered pictures. The pictures depicted the rearward scene of a car following the driver as viewed through a camera at varying vertical and horizontal positions. The following vehicle's size and environmental information varied among conditions and experiments.

RESULTS

Lower camera positions led to distance overestimation and higher positions to underestimation. The effect increased as the distance to the following vehicle decreased. Moreover, larger vehicles led to stronger distance underestimation, especially in low camera positions. Interestingly, the main effect of camera position disappeared when the ego-vehicles' back was visible.

CONCLUSION

Different rearward viewpoints affect distance estimation of drivers, especially in close distances. However, a visible reference of one's own vehicle seems to mostly compensate this effect.

APPLICATION

In general, the rear-view camera should be mounted rather higher and to the front of the vehicle. Also, the vehicle's back should always be visible. Low camera positions are not recommended.

Distressed in the queue? Psychophysiological and behavioral evidence for two alternative car-following techniques

BACKGROUND

Nature offers numerous examples of animal species exhibiting harmonious collective movement. Unfortunately, the motorized Homo sapiens sapiens is not included and pays a price for it. Too often, drivers who simply follow other drivers are caught in the worst road threat after a crash: congestions. In the past, the solution to this problem has gone hand in hand with infrastructure investment. However, approaches such as the Nagoya Paradigm propose now to see congestion as the consequence of multiple interacting particles whose disturbances are transmitted in a waveform. This view clashes with a longlasting assumption ordering traffic flows, the rational driver postulate (i.e., drivers' alleged propensity to maintain a safe distance). Rather than a mere coincidence, the worldwide adoption of the safety-distance tenet and the worldwide presence of congestion emerge now as cause and effect. Nevertheless, nothing in the drivers' endowment impedes the adoption of other car-following (CF) strategies. The present study questions the a priori of safety-distance, comparing two elementary CF strategies, Driving to keep Distance (DD), that still prevails worldwide, and Driving to keep Inertia (DI), a complementary CF technique that offsets traffic waves disturbances, ensuring uninterrupted traffic flows. By asking drivers to drive DD and DI, we aim to characterize both CF strategies, comparing their effects on the individual driver (how he drives, how he feels, what he pays attention to) and also on the road space occupied by a platoon of DD robot-followers.

METHODS

Thirty drivers (50% women) were invited to adopt DD/DI in a driving simulator following a swinging leader. The design was a repeated measures model controlling for order. The CF technique, DD or DI, was the within-subject factor. Order (DD-DI / DI-DD) was the between-subjects factor. There were four blocks of dependent measures: individual driving performance (accelerations, decelerations, crashes, distance to lead vehicle, speed and fuel consumption), emotional dimensions (measures of skin conductance and self-reports of affective states concerning valence, arousal, and dominance), and visual behavior (fixations count and average duration, dwell times, and revisits) concerning three regions of the driving scene (the Top Rear Car -TRC- or the Bottom Rear Car -BRC- of the leading vehicle and the surrounding White Space Area -WSA). The final block concerned the road space occupied by a platoon of 8 virtual DD followers.

RESULTS

Drivers easily understood and applied DD/DI as required, switching back and forth between the two. Average speeds for DD/DI were similar, but DD drivers exhibited a greater number of accelerations, decelerations, speed variability, and crashes. Conversely, DI required greater CF distance, that was dynamically adjusted, and spent less fuel. Valence was similar, but DI drivers felt less aroused and more dominant. When driving DD visual scan was centered on the leader's BRC, whereas DI elicited more attention to WSA (i.e., adopting wider vision angles). In spite of DI requiring more CF distance, the resulting road space occupied between the leader and the 8th DD robot was greater when driving DD.

Semantic modulation of time-to-collision judgments

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.

Pedestrian estimation of their crossing time on multi-lane roads

Estimation of one's own crossing time is an important process in making road-crossing decisions. This study evaluated the pedestrian's (esp. the elderly) ability to estimate crossing time in a field experiment. The estimated crossing time was measured by an interval production method (participants produced an interval to represent their estimated crossing time) and an imagined crossing method. The results showed that while young pedestrians generally had an accurate estimation of their crossing time, old pedestrians consistently underestimated the crossing time in both methods, especially at a wider road. What's worse, even fast walking cannot compensate for the large underestimation. Further analysis showed that although old pedestrians had the declined motor imagery ability and the worse general timing accuracy, none of them can account for the inaccuracy of estimation. These findings suggest that underestimation of crossing time may be one of the important reasons for the acknowledged risky road crossing decision-making in old pedestrians. It also calls for studies on assistive roadway designs and intervention programs targeting old pedestrians.

Can Stroboscopic Training Improve Judgments of Time-to-Collision?

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.

The 'Saw but Forgot' error: A role for short-term memory failures in understanding junction crashes?

Motorcyclists are involved in an exceptionally high number of crashes for the distance they travel, with one of the most common incidents being where another road user pulls out into the path of an oncoming motorcycle frequently resulting in a fatal collision. These instances have previously been interpreted as failures of visual attention, sometimes termed 'Look but Fail to See' (LBFTS) crashes, and interventions have focused on improving drivers' visual scanning and motorcycles' visibility. Here we show from a series of three experiments in a high-fidelity driving simulator, that when drivers' visual attention towards and memory for approaching vehicles is experimentally tested, drivers fail to report approaching motorcycles on between 13% and 18% of occasions. This happens even when the driver is pulling out into a safety-critical gap in front of the motorcycle, and often happens despite the driver having directly fixated on the oncoming vehicle. These failures in reporting a critical vehicle were not associated with how long the driver looked at the vehicle for, but were associated with drivers' subsequent visual search and the time that elapsed between fixating on the oncoming vehicle and pulling out of the junction. Here, we raise the possibility that interference in short-term memory might prevent drivers holding important visual information during these complex manoeuvres. This explanation suggests that some junction crashes on real roads that have been attributed to LBFTS errors may have been misclassified and might instead be the result of 'Saw but Forgot' (SBF) errors. We provide a framework for understanding the role of short-term memory in such situations, the Perceive Retain Choose (PRC) model, as well as novel predictions and proposals for practical interventions that may prevent this type of crash in the future.

Gibson and Crooks (1938): Vision and Validation

Gibson and Crooks (1938) is a landmark article that was ahead of its time, has had sustained and significant impact, and raised issues that are still being considered now. Although most influential in driving research, the concepts Gibson and Crooks presented influenced other domains, including surgery and naval operations. After summarizing the key concepts in Gibson and Crooks, we show how those concepts foreshadowed key principles of Gibson’s ecological approach to visual perception (Gibson, 1979/1986). We then describe research that validates and builds on the analyses of Gibson and Crooks. We conclude that Gibson and Crooks will continue to have impact and generate research for years to come.

Judging arrival times of incoming traffic vehicles is not a prerequisite for safely crossing an intersection: Differential effects of vehicle size and type in passive judgment and active driving tasks

Using a fixed-base driving simulator we compared the effects of the size and type of traffic vehicles (i.e., normal-sized or double-sized cars or motorcycles) approaching an intersection in two different tasks. In the perceptual judgment task, passively moving participants estimated when a traffic vehicle would reach the intersection for actual arrival times (ATs) of 1, 2, or 3s. In line with earlier findings, ATs were generally underestimated, the more so the longer the actual AT. Results revealed that vehicle size affected judgments in particular for the larger actual ATs (2 and 3s), with double-sized vehicles then being judged as arriving earlier than normal-sized vehicles. Vehicle type, on the other hand, affected judgments at the smaller actual ATs (1 and 2s), with cars then being judged as arriving earlier than motorcycles. In the behavioral task participants actively drove the simulator to cross the intersection by passing through a gap in a train of traffic. Analyses of the speed variations observed during the active intersection-crossing task revealed that the size and type of vehicles in the traffic train did not affect driving behavior in the same way as in the AT judgment task. First, effects were considerably smaller, affecting driving behavior only marginally. Second, effects were opposite to expectations based on AT judgments: driver approach speeds were smaller (rather than larger) when confronted with double-sized vehicles as compared to their normal-sized counterparts and when confronted with cars as compared to motorcycles. Finally, the temporality of the effects was different on the two tasks: vehicle size affected driver approach speed in the final stages of approach rather than early on, while vehicle type affected driver approach speed early on rather than later. Overall, we conclude that the active control of approach to the intersection is not based on successive judgments of traffic vehicle arrival times. These results thereby question the general belief that arrival time estimates are crucial for safe interaction with traffic.

Size speed bias or size arrival effect-How judgments of vehicles' approach speed and time to arrival are influenced by the vehicles' size

Crashes at railway level crossings are a key problem for railway operations. It has been suggested that a potential explanation for such crashes might lie in a so-called size speed bias, which describes the phenomenon that observers underestimate the speed of larger objects, such as aircraft or trains. While there is some evidence that this size speed bias indeed exists, it is somewhat at odds with another well researched phenomenon, the size arrival effect. When asked to judge the time it takes an approaching object to arrive at a predefined position (time to arrival, TTA), observers tend to provide lower estimates for larger objects. In that case, road users' crossing decisions when confronted with larger vehicles should be rather conservative, which has been confirmed in multiple studies on gap acceptance. The aim of the experiment reported in this paper was to clarify the relationship between size speed bias and size arrival effect. Employing a relative judgment task, both speed and TTA estimates were assessed for virtual depictions of a train and a truck, using a car as a reference to compare against. The results confirmed the size speed bias for the speed judgments, with both train and truck being perceived as travelling slower than the car. A comparable bias was also present in the TTA estimates for the truck. In contrast, no size arrival effect could be found for the train or the truck, neither in the speed nor the TTA judgments. This finding is inconsistent with the fact that crossing behaviour when confronted with larger vehicles appears to be consistently more conservative. This discrepancy might be interpreted as an indication that factors other than perceived speed or TTA play an important role for the differences in gap acceptance between different types of vehicles.

Audiovisual Integration of Time-to-Contact Information for Approaching Objects

Previous studies of time-to-collision (TTC) judgments of approaching objects focused on effectiveness of visual TTC information in the optical expansion pattern (e.g., visual tau, disparity). Fewer studies examined effectiveness of auditory TTC information in the pattern of increasing intensity (auditory tau), or measured integration of auditory and visual TTC information. Here, participants judged TTC of an approaching object presented in the visual or auditory modality, or both concurrently. TTC information provided by the modalities was jittered slightly against each other, so that auditory and visual TTC were not perfectly correlated. A psychophysical reverse correlation approach was used to estimate the influence of auditory and visual cues on TTC estimates. TTC estimates were shorter in the auditory than the visual condition. On average, TTC judgments in the audiovisual condition were not significantly different from judgments in the visual condition. However, multiple regression analyses showed that TTC estimates were based on both auditory and visual information. Although heuristic cues (final sound pressure level, final optical size) and more reliable information (relative rate of change in acoustic intensity, optical expansion) contributed to auditory and visual judgments, the effect of heuristics was greater in the auditory condition. Although auditory and visual information influenced judgments, concurrent presentation of both did not result in lower response variability compared to presentation of either one alone; there was no multimodal advantage. The relative weightings of heuristics and more reliable information differed between auditory and visual TTC judgments, and when both were available, visual information was weighted more heavily.

Effects of oncoming vehicle size on overtaking judgments

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.

On the relationship between pedestrian gap acceptance and time to arrival estimates

The identification of safe gaps between passing cars when crossing a street is a task most of us accomplish successfully on a daily basis. Objectively, how safe a specific gap is, is mainly dependent on how long it would take the approaching vehicle to arrive (time to arrival; TTA). Common sense might suggest that TTA is the basis for pedestrians' gap selection. However, it has been shown repeatedly that vehicle approach speed has a substantial influence on the size of chosen gaps. At higher speeds, pedestrians tend to accept smaller time gaps, i.e. they initiate riskier crossings. Some researchers have gone so far as to suggest that pedestrians rely more on physical distance of a vehicle in their crossing decisions than TTA. Yet, at the same time, there is evidence that TTA estimates themselves are influenced by object approach speed. It is suspected that pedestrians are more apt to base their decisions on systematically distorted TTA estimates, rather than physical distance. The goal of the two experiments described in this article was to explore the relationship between gap acceptance and TTA estimation. Participants were presented with video clips of approaching vehicles, and were either required to indicate a crossing decision, or to estimate TTA. Results show the typical effects of speed (smaller gaps at higher speed, lower TTA estimate at lower speed) and age (larger gaps for older participants). However, when using subjective time gap size (the TTA estimate) instead of objective time gap size to predict gap acceptance, the effect of speed either disappeared (Experiment I) or decreased substantially (Experiment II). The results indicate that systematic differences in TTA estimates can be a reasonable explanation for the effect of speed on gap acceptance.