Personalized Trajectory Planning for Intelligent Connected Vehicles: A Framework Adapting to Heterogeneous Driving Styles in Complex Interactive Scenarios

Abstract To enhance the safety and adaptability of intelligent connected vehicle (ICV) in complex environments, this study proposes a hierarchical trajectory planning and tracking framework centered on a personalized driving risk field (DRF). The core innovation is quantifiable driver behavior factor integrated into the DRF, enabling it to dynamically modulate risk perception based on inferred driving styles. This mechanism translates behavioral differences into a continuous risk landscape. Within this framework, kinematically feasible trajectories are generated using quintic and quartic polynomials and are optimized by a multiobjective cost function that balances DRF-based safety against comfort and efficiency. A decoupled controller combining lateral linear quadratic regulator and longitudinal dual proportional integral derivative ensures precise tracking. Extensive simulations, including sensitivity analysis and validation in mixed dynamic-static and unsignalized intersection scenarios, demonstrate the framework’s effectiveness. The main findings are: (1) The personalized DRF generates significantly differentiated risk perception, with aggressive vehicles induce a peak field intensity approximately 2.6 times higher than conservative ones. (2) This perception drives intelligent decision-making. For high-risk interactions, the ego vehicle employs earlier and stronger braking to shorten high-risk exposure yet achieves a higher passing speed, demonstrating an optimization strategy beyond safety rules. (3) The controller reliably executes these differentiated commands, with root mean square errors for trajectory, speed, and heading tracking errors remaining below 0.038 m, 0.15 m / s , and 0.0042 rad, respectively. The robust closed-loop performance from decision-making to execution has been demonstrated. The framework enables ICVs to execute context-aware, personalized strategies, enhancing interactive safety and intelligence.