Driving Risk Assessment for Intelligent Vehicles Based on Entropy-Informed Graph Neural Networks and Gaussian Distributions

This study proposes a novel framework based on an entropy-informed graph neural network (EIGNN) integrated with Gaussian distribution (GD) to assess the driving risk of intelligent vehicles in typical traffic scenarios. Existing research often overlooks comprehensive spatiotemporal modeling of vehicle interaction characteristics and the quantification of uncertainty in dynamic risk assessments. In this work, vehicle speed and acceleration are probabilistically modeled using GD, while entropy theory is introduced to quantify risk uncertainty. A risk assessment model based on graph neural networks (GNNs) is then designed to capture the spatiotemporal dynamics of multivehicle interactions and predict the potential risk levels of driving strategies. The results demonstrate that the framework accurately quantifies collision risks in multivehicle interactions in complex traffic scenarios, with high accuracy and robustness across typical situations such as cruising, cut-ins, lane changes, overtaking, and different density traffic. By thoroughly analyzing traffic risk characteristics and incorporating them into intelligent driving decision-making, this study provides significant technical insights and theoretical support for enhancing the safety and decision-making efficiency of autonomous driving systems.