Collaborative Planning for Mixed-Autonomy Lane Merging
Abstract
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.
Cite
@article{arxiv.1808.02550,
title = {Collaborative Planning for Mixed-Autonomy Lane Merging},
author = {Shray Bansal and Akansel Cosgun and Alireza Nakhaei and Kikuo Fujimura},
journal= {arXiv preprint arXiv:1808.02550},
year = {2018}
}
Comments
To appear at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)