A Bi-level Risk-constrained Optimization (BRO) Model for Autonomous Driving Trajectory Planning at On-ramp Areas*

The on-ramp area is a very common bottleneck for trajectory planning of autonomous driving. Existing studies are mostly based on rule computation or behavior cloning of human drivers. This study proposes an optimization-based trajectory planning model to improve merging efficiency and safety. To combine safety and efficiency criteria, this research developed a risk-constrained model based on computation of risk map combining road and traffic characteristics based on dynamic interaction responses of the mainstream vehicle. Dynamic programming (DP) and quadratic programming (QP) are applied to optimize speed profile of merging. To evaluate the algorithm’s performance, CARLA and SUMO are used to create on-ramp scenarios and traffic flow at the on-ramp area. The results reveal that the proposed Bi-level risk-constrained optimization (BRO) model achieves a balance of safety and efficiency maneuvers When compared to the non-optimization-based merging trajectory planning model, the efficiency of traffic flow is effectively improved by 23.3% in low-density traffic flow scenarios and 28.3% in high-density scenarios. The simulation results reveal that the proposed BRO method reduces the overall traffic delay at on-ramp merging area while improving safety performance.