Enhancing Human-Automated Vehicle Interaction in Complex Environments Using Virtual and Extended Reality Technologies

The integration of autonomous vehicles (AVs) into urban ecosystems demands a comprehensive understanding of pedestrian-AV interactions, particularly at unconstrained intersections where unpredictability and communication gaps can compromise safety. This chapter explores critical challenges in AV decision-making, pedestrian behavior modeling, and ethical considerations in dynamic environments. We highlight the potential of Extended Reality (XR) to simulate real-world traffic scenarios, enabling cost-effective testing and behavior analysis. Through a comparative study of simulation tools like CARLA, SUMO, Unity, and Unreal Engine, we propose a virtual framework for designing and evaluating external Human-Machine Interfaces (eHMIs). Emphasizing cognitive modeling, trajectory prediction, and sensor fusion, this chapter contributes to the development of safer, more intuitive AV systems capable of navigating complex pedestrian interactions with increased reliability and public trust.