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.
Cite
@article{arxiv.1707.04489,
title = {Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic},
author = {Tomoki Nishi and Prashant Doshi and Danil Prokhorov},
journal= {arXiv preprint arXiv:1707.04489},
year = {2019}
}
Comments
6 pages, 5 figures. ICML Workshop on Machine Learning for Autonomous Vehicles