A Bounded Rationality-Aware Car-Following Strategy for Alleviating Cut-In Events and Traffic Disturbances in Traffic Oscillations

Neighboring human-driven vehicles (HDVs) frequently perform uncertain cut-in maneuvers, posing a threat to the safety and efficiency of autonomous vehicles (AVs), particularly in traffic oscillation scenarios characterized by AVs experiencing speed disturbances. In this paper, we propose an AV car-following strategy based on bounded rationality-aware reinforcement learning (BRARL) to handle cut-in maneuvers. The approach can handle scenarios involving simultaneous cut-in preclusion and cut-in yielding. Considering the limited rationality of human drivers during lane change, this strategy incorporates a bounded rationality-based game process to restrict discretionary cut-ins while safeguarding AV’s interests, including efficiency, safety, and comfort. The well-designed RL framework captures the exhibited randomness of both the cut-in and preceding HDVs, enabling the AV to effectively handle cut-in maneuvers and reduce speed disturbances. Simulated experiments demonstrate the high generalization capability of our strategy in reducing preceding speed disturbances (e.g., achieving a minimum reduction of 34.5% in traffic disturbances compared to two baselines), and preventing discretionary cut-in maneuvers.