Collaborative Planning for Mixed-Autonomy Lane Merging
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