Lane changing is a fundamental but challenging operation for moving vehicles. Connected and Automated Vehicles(CAVs) enable autonomous vehicles to cooperate with each other to accomplish the lane changing tasks, profiting from their communication ability. However, dispatching CAVs in mixed traffic remains difficult due to the stochastic behaviors and uncertain intentions of Human-Driven Vehicles(HDVs). To tackle this issue, this paper devises a coordination approach based on Conflict-Based Search(CBS) theory. Firstly, HDVs are accurately modeled as constraints to enable usage of CBS in the mixed traffic. Additionally, virtual goals are introduced to search CAVs’ priority and outlets along with path finding. Furthermore, we optimize the performance of CBS in dense traffic by defining the concept of following vehicles. Experiments show that performance is improved by utilizing new conflict prioritizing rules and a heuristic value calculation method that derived from following vehicles. Finally, we introduce grouping vehicles to extend the proposed method for solving extremely dense and large instances at a scale of more than one hundred without significant loss in efficiency.

Human-driven vehicles (HDVs) will still exist for a long time as we move towards the era of connected autonomous vehicles (CAVs). It is challenging to ensure the safety of the system and improve the efficiency of convoys in mixed traffic environments due to the stochastic behaviors and uncertain intentions of HDVs. To address these issues, this paper develops an uncertainty-aware cooperative lane-changing framework, termed UCLF, for CAVs based on partially observable Markov decision process (POMDP). We extend POMDP to multi-agent cooperative lane-changing by prioritizing CAVs according to lane-changing urgency and planning for CAVs sequentially. Two novel cooperation mechanisms, namely cooperative implicit branching and cooperative explicit pruning, are proposed to promote efficiency and ensure safety. Numerical experiments are conducted to show the smooth and efficient lane-changing maneuvers under intention uncertainty. Compared to baseline, UCLF achieves up to 28.7% decrease in total travel time on average. We also validate UCLF in a real multi-AGV (Automated Guided Vehicle) system to demonstrate the usability and reliability of our study.