A dynamic dual optimization method for safe lane changing in autonomous driving

Safe lane changing is critical for the automated driving technology. Current research on lane-changing trajectory planning exhibits drawbacks such as inaccurate environmental perception, insufficient system robustness, causing risks in the lane-changing process of autonomous vehicles. This paper proposes a dynamic assessment dual planning framework to enhance lane-change safety. Firstly, the Highway Drone (HighD) dataset is utilised to extract lane-changing events, and the iTransformer model is employed to predict the future trajectories of surrounding vehicles. Secondly, a spatiotemporal risk assessment method is introduced, incorporating accident probability, severity, and interaction intensity to weight inter-vehicle risks. Finally, a dual-planning module integrates risk factors with comfort, safety, and dynamic constraints using a quintic polynomial trajectory model optimized by particle swarm optimization. The model is validated on HighD dataset, showing its effectiveness in reducing lane-change risk and enhancing autonomous driving safety and efficiency.