ICOPP: A Highway Autonomous Driving Planner Based on Interactive Collaborative Optimization and POMDP

Abstract In this paper, we propose a planner for autonomous vehicles in complex highway scenarios, which can efficiently plan the trajectory of autonomous vehicles. The method aims to improve the intelligence of autonomous driving under the uncertainty of the perception and prediction system. The method employs a POMDP framework to address the challenges brought about by uncertainty. Firstly, this method divides the entire planning cycle into several discrete decision-making processes. In each process, the vehicle’s lateral and longitudinal behaviors are semantically processed to obtain a limited number of decision sequences, thereby addressing the curse of dimensionality in state space and observation space in POMDP problems. In each decision-making process, different simulation scenarios are constructed based on the decision of the self-vehicle and probability distribution of the predicting behaviors of surrounding vehicles. In each scenario, key vehicles are selected for collaborative optimal planning among multiple vehicles, resulting in the optimal drivable trajectories for both the ego vehicle and other vehicles under the given decision. Finally, an evaluation of the policy is conducted in terms of safety, comfort, and efficiency to select the optimal policy. In simulation experiments, the ICOPP demonstrates smoother and more stable autonomous driving performance compared to traditional methods.