Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
This addresses the challenge of deadlock avoidance in autonomous driving for dense traffic scenarios, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of autonomous vehicle decision-making in dense merging traffic by developing a reinforcement learning approach that models other drivers' cooperation levels, resulting in fewer deadlocks compared to online planning methods.
Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.