When crossing a road, pedestrians must detect traffic, combine data coming from different perceptual modalities, evaluate the time envelope for safely cross the street, and monitor the position of oncoming vehicles to perform corrective actions if needed. This study analyzed the influence of noise emitted by vehicles, or its absence, on pedestrians’ crossing decision-making. Experiments were performed in a virtual environment using two road scenarios. Participants were presented with stimuli of approaching vehicles that varied regarding speed, movement patterns, and auditory condition: one concerning the approaching of an electric vehicle, another regarding the approaching of a gasoline combustion vehicle, and, finally, a condition regarding the absence of auditory cues. Participants were tasked with indicating the moment when they decided to cross the street. The results show that, despite the noise variations caused by the type of vehicle and its speed pattern, the participants’ decision to cross was mostly based on vehicle distance. When a vehicle approaches the crosswalk from a short distance and with no occlusion to the pedestrian’s visibility, the sound does not seem to influence the pedestrians’ crossing decision-making.

The advent of autonomous vehicles (AVs) has sparked many concerns about pedestrian safety, prompting manufacturers and researchers to integrate external Human–Machine Interfaces (eHMIs) into AVs as communication tools between vehicles and pedestrians. The evolving dynamics of vehicle–pedestrian interactions make eHMIs a compelling strategy for enhancing safety. This study aimed to examine the contribution of eHMIs to safety while exploring the impact of an incentive system on pedestrian risk behavior. Participants interacted with AVs equipped with eHMIs in an immersive environment featuring two distinct scenarios, each highlighting a sense of urgency to reach their destination. In the first scenario, participants behaved naturally without specific instructions, while in the second scenario, they were informed of an incentive aimed at motivating them to cross the road promptly. This innovative experimental approach explored whether motivated participants could maintain focus and accurately perceive genuine risk within virtual environments. The introduction of a reward system significantly increased road-crossings, particularly when the vehicle was approaching at higher speeds, indicating that incentives encouraged participants to take more risks while crossing. Additionally, eHMIs notably impacted pedestrian risk behavior, with participants more likely to cross when the vehicle signaled it would not stop.

Virtual studies involving pedestrians have gained relevance due to the advantage of not exposing them to actual risk, and simulation setups have benefitted from rapid technical advancements, becoming increasingly complex and immersive. However, it remains unclear whether complex setups affecting participants’ freedom of movement impact their decision-making. This research evaluated the effects of a more realistic approach to studying pedestrian crossing behavior by comparing a perception-action task requiring participants to walk effectively along a semi-virtual crosswalk with a similar experiment using static crossing conditions. Using a CAVE system, two real-world streets were modeled in two different virtual scenarios, varying vehicle speed patterns and distance from the crosswalk. Visual stimuli were presented to two groups of 30 participants, with auditory stimuli adapted accordingly. The impact of various factors on participants’ crossing decisions was evaluated by examining the percentage of crossings, crossing start time, and time-to-passage. Overall, the experimental approach did not significantly affect participants’ crossing decisions.

As autonomous vehicles (AVs) become part of urban environments, pedestrian safety and interactions with these vehicles are critical to creating sustainable, walkable cities. Intuitive pedestrian-vehicle communication is essential not only for reducing crash risk but also for supporting policies that promote active mobility and efficient traffic flow. This study investigates pedestrian crossing behavior in a fully immersive virtual reality environment, building on previous work by the authors conducted in a CAVE-type simulator. Participants crossed between a conventional vehicle and an AV when they perceived it was safe. The analysis examines how external human–machine interfaces (eHMIs) influence crossing decisions, collisions, safety margins, and crossing initiation time (CIT) across different vehicle speeds and traffic gaps. Three hypotheses were tested regarding the effects of eHMIs on CIT, risk-taking behavior, and perceived safety. Results show that eHMIs significantly affect pedestrian decisions: participants delayed crossings when the eHMI indicated non-yielding behavior and initiated crossings earlier when yielding was signaled. Risk-taking behavior increased at higher vehicle speeds and shorter time gaps. Although perceived safety did not increase, behavioral results indicate reliance on visual cues. These findings underscore the importance of standardizing eHMIs to support pedestrian safety and sustainable urban mobility.