A Local Trajectory Planning Method Based on Asymmetric Driving Aggressiveness Model

Conventional trajectory planning methods encounter various challenges: Inability to better distinguish different types of vehicles, and failure to consider the difference between perceived threats or risks during asymmetric and symmetric interactions for autonomous vehicles. To solve these issues, the insufficiency of the traditional risk-field model is analyzed, and an asymmetric aggressiveness model is investigated in this study, which quantifies the suffered aggressiveness of vehicles. Then, the asymmetric aggressiveness model and the static potential risk field describing the road structure are used as the control objectives of the optimal controller to avoid collisions. Furthermore, a three-degree-of-freedom vehicle dynamics model is constructed, and the optimal feasible trajectory is planned by using the model predictive control algorithm. Finally, the results demonstrate that the proposed trajectory planning method can reliably plan trajectories to avoid collisions with environmental vehicles in complex scenarios, including car-following and lane-changing while satisfying the vehicle dynamics constraints. Moreover, the proposed method also can generate the differentiated driving trajectories when interacting with different types of vehicles.