Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
This addresses a key challenge in autonomous vehicles, though it appears incremental as it builds on existing reinforcement learning techniques for a specific domain.
The paper tackles the problem of freeway merging in congested traffic for automated driving by proposing a multi-policy decision-making method using passive actor-critic reinforcement learning, achieving a 92% success rate comparable to human performance.
Freeway merging in congested traffic is a significant challenge toward fully automated driving. Merging vehicles need to decide not only how to merge into a spot, but also where to merge. We present a method for the freeway merging based on multi-policy decision making with a reinforcement learning method called {\em passive actor-critic} (pAC), which learns with less knowledge of the system and without active exploration. The method selects a merging spot candidate by using the state value learned with pAC. We evaluate our method using real traffic data. Our experiments show that pAC achieves 92\% success rate to merge into a freeway, which is comparable to human decision making.