Research on intelligent connected vehicles lane change trajectory optimization based on risk field in mixed traffic

This study investigates the optimization of lane-change trajectories for intelligent connected vehicles (ICVs) operating in mixed traffic environments. By integrating dynamic risk modeling with cooperative obstacle-avoidance planning, lane-change safety is enhanced while maintaining trajectory stability.

A comprehensive dynamic risk-field model was developed. The relative motion states of surrounding vehicles and their geometric occupancy were mapped into a continuous spatial risk distribution. This formulation characterizes the spatial propagation and attenuation of interaction risks and describes their spatiotemporal evolution during lane-change maneuvers. On this basis, a multi-objective trajectory optimization model was established within a path-velocity decoupling framework. Risk-field functions were incorporated together with vehicle dynamic feasibility constraints. Optimal lane-change trajectories were then generated through cost minimization. The effectiveness of the proposed risk-driven optimization approach was evaluated by comparing risk levels before and after optimization.

The results indicate that the composite interaction risk during lane-change maneuvers follows the order: longitudinal following phase > lane-change execution phase > lane-change preparation phase > post-change adjustment phase. The proposed risk-driven optimization method achieves a balance between risk mitigation and dynamic responsiveness. Smooth and stable lane-change trajectories are generated for ICVs. Compared with the original trajectories, the comprehensive interaction risk decreased by 0.86%, 0.55%, 3.44%, and 5.62% across the four respective phases. In addition, high-risk regions contracted, and the distribution of risk gradients became more uniform.

The proposed risk-field-driven multi-objective trajectory optimization method quantitatively characterizes the evolution of interaction risk during lane changes in mixed traffic. Interaction risk levels are effectively reduced through the proposed framework. Trajectory smoothness and control stability are improved while dynamic feasibility is strictly maintained. The method thus provides theoretical support for intelligent connected vehicles to execute safe lane-change maneuvers in complex multi-vehicle interaction environments.