The number of road accidents is decreasing slightly in developed countries, mainly due to technological advancements and government actions. However, accidents involving vulnerable users, such as pedestrians, remain high. Autonomous vehicles (AVs) are expected to benefit pedestrians, though their interaction with pedestrians raises questions. Common driver behaviors like gestures, eye contact, and flashing lights indicate a willingness to yield, which AVs must replicate. Our research compares two experiments: a virtual reality (VR) pedestrian crossing with an LED display on a virtual AV and a real traffic scenario using the same LED display on an actual car. We investigated how much pedestrians rely on LED communication and whether there are differences between VR and real-world settings. A questionnaire gathered demographic data and trust levels in LEDs. The VR experiment had 51 participants, while the real traffic experiment involved 136 pedestrians. Overall, 82% responded positively to the LED display, with gender and age being insignificant factors. A rapid learning process indicated that explicit communication patterns were self-explanatory. In the VR experiment, 75% moderately trusted the LED display, while 18% fully trusted it. In real traffic, 44% fully trusted the display after familiarization, but skepticism was higher compared to the VR setting.

Since autonomous vehicles (AV) are in the testing process, it is an open question of how pedestrians will communicate with self-driving cars. Nowadays, explicit communication pattern is the main way of pedestrian-driver interaction, however, AV may use implicit communication when making crossing decisions. This study aims to analyze pedestrians’ gaze behavior when crossing the road using an eye camera and find the most applicable location for the LED interface on AVs. 10 pedestrian crossings in Gyor, Hungary were analyzed using the synchronized eye-tracking (ET) technology and regular video cameras for combined data processing. The data were analyzed using digital image processing techniques and statistical methods to identify where pedestrians looked and whether a pedestrian-driver interaction was captured during the crossing maneuver.

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.