Dynamic Trajectory Planning for Autonomous Vehicle Considering Driving Risk Field

Autonomous vehicles face difficulties in planning reasonable routes to avoid risks because of increasingly complex road conditions. We propose a novel and systematic method to assess driving risk and use the MPC algorithm to plan driving trajectories dynamically. To assess driving risk, we build a driving risk field that includes potential energy field, kinetic energy field, and behavioral field. Approaching the target, reducing driving risk, and keeping vehicle stability are the optimization goals in the process of trajectory planning. By solving the optimization problem in current time, we obtain control variables such as front-wheel rotation angle. Using current autonomous vehicle information to predict position at the next moment, we generate autonomous vehicle trajectory planning in real-time. Simulation results show that the algorithm designed in this paper can achieve safe trajectory planning for autonomous vehicles. The new method is more suitable for vehicle dynamics models and generates smoother paths.