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

Should I stay or should I go? The cerebral bases of street-crossing decision

An observer willing to cross a street must first estimate if the approaching cars offer enough time to safely complete the task. The brain areas supporting this perception, known as Time-To-Contact (TTC) perception, have been mainly studied through noninvasive correlational approaches. We carried out an experiment in which patients were tested during an awake brain surgery electrostimulation mapping to examine the causal implication of various brain areas in the street-crossing decision process. Forty patients were tested in a gap acceptance task before their surgery to establish a baseline performance. The task was individually adapted upon this baseline level and carried out during their surgery. We acquired and normalized to MNI space the coordinates of the functional areas that influenced task performance. A total of 103 stimulation sites were tested, allowing to establish a large map of the areas involved in the street-crossing decision. Multiple sites were found to impact the gap acceptance decision. A direct implication was however found mostly for sites within the right parietal lobe, while indirect implication was found for sites within the language, motor, or attentional networks. The right parietal lobe can be considered as causally influencing the gap acceptance decision. Other positive sites were all accompanied with dysfunction in other cognitive functions, and therefore should probably not be considered as the site of TTC estimation.

Following car reduces motorcycles' size-arrival effect: A study using online experiments

Many fatal motorcycle accidents occur because car drivers infringe on motorcycle riders' right-of-way. The size-arrival effect refers to observers' tendency to judge larger objects as arriving sooner than smaller objects when estimating an approaching object's arrival time, which is one cause of right-of-way motorcycle accidents. Previous research has focused on a single vehicle that approaches the driver. However, it is also possible that a motorcycle approaches a driver along with other vehicles driving on multiple-lane roads. This paper presents the results of two online experiments; Experiment 1 validated the size-arrival effect when either a car or a motorcycle approached a driver waiting to turn across an intersection; and Experiment 2 investigated the size-arrival effect when a motorcycle and a parallelly driven car simultaneously approached the driver. Participants (n = 1723) in Experiment 1 and (n = 986) in Experiment 2 took part in the study. The results (1) validated the size-arrival effect; drivers accepted a smaller gap for approaching motorcycles than cars; (2) in the present settings drivers made turn decisions based on the distance gap rather than the time-to-arrival gap; (3) driver's acceptance of the gap when facing a motorcycle and a car was comparable to when facing only the car and significantly larger than facing only a motorcycle. These findings indicate that a car driving parallelly or behind a motorcycle reduces the size-arrival effect. These findings provide implications to suggest a safe riding strategy for motorcycle riders. We also suggest that such online experiments would facilitate studying large samples with less effort.

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.

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.

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

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.

Design of a hesitant movement gesture for mobile robots

In previous experiments, a back-off movement was introduced as a motion strategy of robots to facilitate the order of passage at bottlenecks in human-robot spatial interaction. In this article we take a closer look at the appropriate application of motion parameters that make the backward movement legible. Related works in distance perception, size-speed illusions, and viewpoint-based legibility considerations suggest a relationship between the size of the robot and the observer’s perspective on the expected execution of this movement. We performed a participant experiment (N = 50) in a virtual reality environment where participants adjusted the minimum required back-off length and preferred back-off speed as a result of the robot size, and the viewpoint of the back-off movement. We target a model-based approach on how appropriate back-off design translates to different sized robots and observer’s viewpoints. Thus, we allow the application of back-off in a variety of autonomous moving systems. The results show a significant correlation between the increasingly expected back-off lengths with increasing robot size, but only weak effects of the viewpoint on the requirements of this movement. An exploratory analysis suggests that execution time might be a promising parameter to consider for the design of legible motion.

Visual information of vehicle velocity acquired by pedestrians involved in road crossing accidents

This paper describes a geometric optical relationship between the perceived visual information of approaching vehicles by pedestrians who intend to cross the road and the factors underlying pedestrian accidents in Japan. We create a model based on this visual information, wherein the retinal image corresponding to vehicle velocity perception is broken down into tangential components and normal components in a two-dimensional polar coordinate system that employs the nodal point of the eyeball as the origin. Our visual model uses the relationship between the tangential and normal components of the velocity to calculate the distance at which the velocity of the vehicle can be perceived by pedestrians. The maximum distance at which vehicle velocity can be perceived by pedestrians derived from the visual model is consistent with the timing at which a vehicle collides with a pedestrian most frequently in the pedestrian accidents gleaned from accident statistics. The result of the simulation of the visual model showed that the eye height of the pedestrian, the total height of the vehicle and the sensory threshold of motion determine the components by which the maximum perception distance of the vehicle velocity is given. These findings contribute to the enhancement of safety measures in traffic accidents from the pedestrian's perspective.