AI-Based Solutions for Improving Vehicle-to-Pedestrian Communication

Abstract

Urban mobility today places heavy emphasis on the interactions among vehicles and pedestrians in the street. Effective vehicle–pedestrian communication in these interactions allows various road users to understand the surrounding traffic environment, predict the actions of the traffic participants, and further improve interaction safety. Therefore, more and more research efforts have been attracted to interpret vehicle-to-pedestrian communication as a framework for the audio and visual perception of street participants, signal processing, and action generation for effective communication. The topic is also relevant to the general discussion on road safety and human considerations. The general objectives of our research are to shed light on the importance of communication between autonomous systems and the rest of the road users, with a specific focus on pedestrians, and to explore AI tools aimed at enhancing vehicle-to-pedestrian communication. In line with the above, the paper first sheds light on opportunities to improve driving acceptability and accessibility. In fact, we consider that road vehicles are nodes within a wireless communication network. They have the potential to substantially improve pedestrian safety and driving experience by effectively and intuitively communicating important and possibly safety-critical information with other vehicles, pedestrians, and surrounding infrastructure. Finally, we discuss the features of the reviewed data, the adopted methodology, and the addressed AI-based data processing tools and methodologies, with the primary focus on artificial neural networks.