Estimating time-to-contact when vision is impaired

The role of arousal in the estimation of time-to-collision of threatening stimuli

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.

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.

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.

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.

Wider is better but sharper is not: optimizing the image of camera-monitor systems

The replacement of rear-view mirrors with camera-monitor systems introduces new opportunities for design, such as altering the image quality and the rearward field-of-view. We investigated how the image quality and field-of-view might affect the distance and time-to-contact estimation of other vehicles. Eighty-six subjects estimated either their egocentric distance to a stationary vehicle (Experiment I) or the time-to-contact to an approaching vehicle (Experiment II). Throughout the experiments, the pixel density and either the field-of-view or the viewing condition varied. A larger field-of-view increased distance estimation accuracy and confidence. Reduced pixel density led to larger estimates. In contrast, reduced pixel density and simulated dirt shortened time-to-contact estimates. This is compatible with a safety strategy applied under conditions of impaired vision. Moreover, a limited benefit was observed for higher pixel densities. Therefore, camera-monitor systems with large field-of-view and a pixel density of around 300 ppi could ensure accurate TTC and distance estimation. Practitioner summary: A camera's field-of-view and image quality are important parameters for camera-monitor systems. In two experiments, we investigated the effects of these two parameters on rearward distance and time-to-contact estimation. Whereas a larger field-of-view improved distance estimation accuracy, increasing the pixel density had a limited effect in the estimation of time-to-contact.