Collaborative Planning for Mixed-Autonomy Lane Merging
This addresses lane merging efficiency in mixed-autonomy traffic, but it is incremental as it builds on existing collaborative planning concepts.
The paper tackled the problem of lane merging in mixed-autonomy traffic by proposing a planning framework with a selfishness factor to balance rewards between human-driven and autonomous vehicles, finding in a user study with 21 subjects that balanced factors reduced merging times for both agents.
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.