Deciding when to cross in front of an autonomous vehicle: How child and adult pedestrians respond to eHMI timing and vehicle kinematics

How autonomous vehicles (AVs) communicate their intentions to vulnerable road users (e.g., pedestrians) is a concern given the rapid growth and adoption of this technology. At present, little is known about how children respond to external Human Machine Interface (eHMI) signals from AVs. The current study examined how adults and children respond to the combination of explicit (eHMI signals) and implicit information (vehicle deceleration) to guide their road-crossing decisions. Children (8- to 12-year-olds) and adults made decisions about when to cross in front of a driverless car in an immersive virtual environment. The car sometimes stopped, either abruptly or gradually (manipulated within subjects), to allow participants to cross. When yielding, the car communicated its intent via a dome light that changed from red to green and varied in its timing onset (manipulated between subjects): early eHMI onset, late eHMI onset, or control (no eHMI). As expected, we found that both children and adults waited longer to enter the roadway when vehicles decelerated abruptly than gradually. However, adults responded to the early eHMI signal by crossing sooner when the cars decelerated either gradually or abruptly compared to the control condition. Children were heavily influenced by the late eHMI signal, crossing later when the eHMI signal appeared late and the vehicle decelerated either gradually or abruptly compared to the control condition. Unlike adults, children in the control condition behaved similarly to children in the early eHMI condition by crossing before the yielding vehicle came to a stop. Together, these findings suggest that early eHMI onset may lead to riskier behavior (initiating crossing well before a gradually decelerating vehicle comes to a stop), whereas late eHMI onset may lead to safer behavior (waiting for the eHMI signal to appear before initiating crossing). Without an eHMI signal, children show a concerning overreliance on gradual vehicle deceleration to judge yielding intent.

Communication between Autonomous Vehicles and Pedestrians: An Experimental Study Using Virtual Reality

One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians' reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.

Interaction between pedestrians and automated vehicles: Perceived safety of yielding behaviors and benefits of an external human–machine interface for elderly people

This study focuses on Automated Vehicles (AVs) interactions with pedestrians during road crossing situations. A dual-phase experiment was designed: one from the pedestrian’s perspective and the other one from the AV passenger’s point of view. Eight AV behaviors to yield were investigated. Participants’ task was to assess the safety of each one of these yielding behaviors. Moreover, an external HMI (eHMI) was designed to support them in these interactions. 40 participants were involved in this experiment (50% females, 20 young versus 20 elderly). Results obtained show significant differences between old and young participants: elderly people have not the same way to perceive and assess the safety of the yielding behaviors from “the inside” and from “the outside” of the car. Conversely, young participants assessed AV behaviors similarly whether as pedestrians or as AV passengers. When considering benefits introduced by the eHMI, it significantly reduces differences between old and young participants and tends to harmonize their safety assessments: with to the eHMI, elderly people are more able to adequately perceive and assess the safety/dangerousness of the AV braking manoeuvers, and their safety judgments become at last quite similar to those of young participants. Moreover, the eHMI increases participants’ Acceptance of AV and reduces their concerns about their future interactions with AV as a pedestrian, especially for elderly people.

Crossing the street in front of an autonomous vehicle: An investigation of eye contact between drivengers and vulnerable road users

Communication between road users is a major key to coordinate movement and increase roadway safety. The aim of this work was to grasp how pedestrians (Experiment A), cyclists (Experiment B), and kick scooter users (Experiment C) sought to visually communicate with drivengers when they would face autonomous vehicles (AVs). In each experiment, participants (n = 462, n = 279, and n = 202, respectively) were asked to imagine themselves in described situations of encounters between a specific type of vulnerable road user (e.g., pedestrian) and a human driver in an approaching car. The human driver state and the communicative means of the approaching car through an external Human-Machine Interface (eHMI) were manipulated between the scenarios. The participants were prompted to rate from “never” to “always” (6-point Likert scale) the frequency with which they would seek eye contact with the human driver either in order to express their willingness to cross or to make their effective decision to cross. Our findings revealed that a passive human driver in an AV with no visual checking on the road triggered a decline in vulnerable road users’ desire to communicate by eye contact (Experiments A–C). Moreover, the results of Experiment C demonstrated that the speed screen, the text message screen, and the vibrating mobile app eHMI signals diminished kick scooter users’ desire to communicate visually with the human driver, with some age-based differences. This suggested a better comprehension of the approaching car’s intentions by the kick scooter users, driven by the features of the eHMI.

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.

A Video-Based, Eye-Tracking Study to Investigate the Effect of eHMI Modalities and Locations on Pedestrian–Automated Vehicle Interaction

Numerous studies have emerged on the external human–machine interface (eHMI) to facilitate the communication between automated vehicles (AVs) and other road users. However, it remains to be determined which eHMI modality and location are proper for the pedestrian–AV interaction. Therefore, a video-based, eye-tracking study was performed to investigate how pedestrians responded to AVs with eHMIs in different modalities (flashing text, smiley, light band, sweeping pedestrian icon, arrow, and light bar) and locations (grill, windshield, and roof). Moreover, the effects of pedestrian-related factors (e.g., gender, sensation-seeking level, and traffic accident involvement) were also included and evaluated. The dependent variables included pedestrians’ clarity-rating scores towards these eHMI concepts, road-crossing decision time, and gaze-based metrics (e.g., fixation counts, dwell time, and first fixation duration). The results showed that the text, icon, and arrow-based eHMIs resulted in the shortest decision time, highest clarity scores, and centralized visual attention. The light strip-based eHMIs yielded no significant decrease in decision time yet longer fixation time, indicating difficulties in comprehension of their meaning without learning. The eHMI location had no effect on pedestrians’ decision time but a substantial influence on their visual searching strategy, with a roof eHMI contradicting pedestrians’ inherent scanning pattern. These findings provide implications for the standardized design of future eHMIs.

Take It to the Curb: Scalable Communication Between Autonomous Cars and Vulnerable Road Users Through Curbstone Displays

Automated driving will require new approaches to the communication between vehicles and vulnerable road users (VRUs) such as pedestrians, e.g., through external human–machine interfaces (eHMIs). However, the majority of eHMI concepts are neither scalable (i.e., take into account complex traffic scenarios with multiple vehicles and VRUs), nor do they optimize traffic flow. Speculating on the upgrade of traffic infrastructure in the automated city, we propose Smart Curbs, a scalable communication concept integrated into the curbstone. Using a combination of immersive and non-immersive prototypes, we evaluated the suitability of our concept for complex urban environments in a user study (N = 18). Comparing the approach to a projection-based eHMI, our findings reveal that Smart Curbs are safer to use, as our participants spent less time on the road when crossing. Based on our findings, we discuss the potential of Smart Curbs to mitigate the scalability problem in AV-pedestrian communication and simultaneously enhance traffic flow.

Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment

The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.