2018/08/07 by Shray Bansal, Akansel Cosgun, Bansal, Shray +5
Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Robotics (cs.RO) #Traffic control and management #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.1808.02550
openalex publication_date 2018/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.