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 V ehicle (HV) and an Autonomous V ehicle (A V) drive together . We propose a planning framework where the degree to which the A V considers the other agent's reward is controlled by a selfishness factor . We test our approach on a simulated two-lane highway where the A V 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 nonzero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic. Driving is a social activity: drivers indicate their willingness to change lanes by subtle cues such as eye contact, or by not-so-subtle cues such as adjusting their speed and position [1]. There has been impressive demonstrations of Autonomous V ehicle (A V) technology [2]-[4], however one of the remaining challenges in this area is reading those cues to estimate the intentions of other agents as well as using cues to communicate the intentions of the A V . As A Vs become commonplace, the situations where A V's and Human-driven V ehicles (HV) interact will increase.