UCLF: An Uncertainty-Aware Cooperative Lane-Changing Framework for Connected Autonomous Vehicles in Mixed Traffic

Abstract

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.