Research on trajectory prediction of vehicle lane change for autonomous driving based on inverse reinforcement learning

Autonomous vehicles improve the safety and efficiency of vehicles in complex traffic scenarios through autonomous decision-making intelligence technology. To address the requirements of the self-driving vehicle lane change scenario for the accuracy of vehicle lane change trajectory prediction, in this paper, we propose a lane change trajectory prediction method for self-driving vehicles based on inverse reinforcement learning. We model the inverse reinforcement learning process through a maximum entropy mechanism to learn the optimal reward function that infers the potential end targets during the vehicle lane change. This reward model is used to construct the optimal policy that can be sampled for planning in the grid world. Conditioned on the sequence of state actions sampled by this maximum entropy policy, we generate vehicle lane change prediction trajectories. We conduct training experiments on lane change scenario data from the publicly available nuScenes dataset for autonomous driving, which shows that our method can meet the vehicle lane change requirements in real scenarios and validate the accuracy and reasonableness of the lane change trajectories.