基于多模型障碍物轨迹融合预测的自动驾驶横纵向联合运动规划算法OA

To address the problem that trajectory predictions for traffic participants produced by traditional motion planning algo-rithms are unsuitable for complex driving scenarios and are not effectively integrated with subsequent motion planning,resulting in incomplete utilization of obstacle position information,a longitudinal and lateral joint motion planning algorithm for autonomous driving is proposed based on multi-model fusion prediction of obstacle trajectories.First,the Constant Acceleration(CA)model and the Constant Turn Rate and Velocity(CTRV)model are selected respectively as the long-term and short-term prediction models for traffic participants,and the prediction results are fused using a method based on the Kalman filter.Second,the spatio-temporal occupancy within the prediction horizon is gridded,and,using the obstacle trajectories obtained from the fusion predic-tion,a dynamic programming algorithm is executed to obtain new feasible boundaries.Third,a Linear Time-Varying(LTV)ve-hicle dynamics model is established and the ego vehicle's global trajectory is parameterized;a classical Model Predictive Control(MPC)problem is then formulated and solved via quadratic programming to achieve joint longitudinal and lateral motion planning that yields the desired collision-free motion.Finally,joint simulations are performed on a verification platform built with CarSim software and Simulink software in a three-lane driving scenario.The results indicate that the proposed longitudinal and lateral joint motion planning algorithm based on multi-model fusion prediction of obstacle trajectories can effectively integrate obstacle vehicle trajectory prediction with the ego vehicle's joint motion generation task.The multi-model fusion prediction algorithm dem-onstrates faster response and smaller prediction errors in continuous lane-change scenarios,providing a reference for research on motion planning of autonomous vehicles in dynamic obstacle environments.