The integration of autonomous vehicles (AVs) into traffic road requires careful consideration of their ability to adapt to other road users. Among road users, pedestrians are particularly vulnerable and are likely to interact with AVs. While most studies have focused on pedestrian behavior in interactions with human-driven vehicles, the effects of AV presence and Vehicle-to-Pedestrian (V2P) communication on pedestrian behavior remain largely unexplored. This article explores the impact of these factors, as well as the influence of the learning process induced by the introduction of AVs into traffic, on pedestrian behavior. To this end, we analyze data from our experimental campaign involving 100 participants in a virtual reality (VR) environment designed to address this issue. This study provides a comprehensive analysis of pedestrian behavior, constituting a significant advancement in the understanding of pedestrian-autonomous vehicle interactions and contributing to the integration of AV.

As vehicle systems become increasingly connected and intelligent, insurance providers are turning to machine learning techniques to personalize billing based on individual driving behavior. This shift raises important questions about how to balance predictive performance with user privacy. In this paper, we present PrivFedProfiling, a decentralized privacy-preserving learning framework designed for use-based insurance (UBI) systems. Our method leverages Federated Learning (FL) to collaboratively train behavior models across distributed driver devices without transferring raw data. To further strengthen privacy, we integrate Differential Privacy (DP) and Homomorphic Encryption (HE) within the training process, protecting sensitive patterns in shared model updates. The proposed approach uses a Multilayer Perceptron (MLP) architecture and is validated using synthetic driving behavior data generated from the SUMO simulator. It offers a realistic yet controllable environment for testing. Results indicate that our method maintains high model accuracy while ensuring strong privacy guarantees, making it suitable for real-world deployment.

Pedestrians, as vulnerable road users, pose safety challenges for autonomous vehicles (AVs). Their behavior, often unpredictable and subject to change, complicates AV-pedestrian interactions. To address this uncertainty, AV s can enhance safety by communicating their planned trajectories to pedestrians. In this research, we explore the interaction between pedestrians and autonomous vehicles within an industrial environment, focusing on how communicative behavior from the vehicles influences pedestrians' physiology. We investigate the possibility of mea-suring biosignals while participants wear a VR headset and experiment a pedestrian crossing. Our preliminary study reveals subtle variations in delta rhythms when users immersed in VR simulations interact with AV s that either provide or withhold additional information.

The technology of Connected and Autonomous Vehicles (CAV) is a hot topic of transportation systems, especially regarding platooning and the interaction with other road users. Considering traffic safety, many studies have been devoted to the exchange of information among various road users, such as CAVs and pedestrians. In a platooning scenario, when a pedestrian is detected by a CAV, the leader CAV shares the information with its followers to provide a safe and courteous environment thanks to its connectivity. However, the possibility to improve traffic efficiency while meeting the safety requirements has rarely been addressed in current research. Yet, in industrial areas, where automated vehicles and pedestrians frequently interact, combining safety and efficiency is crucial. The present paper addresses this challenge by first analyzing the intersection of CAVs and pedestrians in no-traffic-signal scenarios. The optimal state is proposed to reduce the time loss. Then, the paper uses a reinforcement learning-based method to make CAVs arrive at the optimal state, to improve traffic efficiency. The experimental results based on virtual reality show that the proposed method increases traffic efficiency while ensuring traffic safety.