The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach.
@article{arxiv.2506.23999,
title = {Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles},
author = {Zeyu Han and Mengchi Cai and Chaoyi Chen and Qingwen Meng and Guangwei Wang and Ying Liu and Qing Xu and Jianqiang Wang and Keqiang Li},
journal= {arXiv preprint arXiv:2506.23999},
year = {2025}
}