Collaborative Planning for Mixed-Autonomy Lane Merging

TL;DR

This paper introduces a planning framework for mixed-autonomy lane merging that balances individual and shared rewards, demonstrating improved merging efficiency and highlighting the potential for collaborative decision-making in autonomous driving.

Contribution

It presents a novel planning approach controlling the level of cooperation between human-driven and autonomous vehicles in lane merging scenarios.

Findings

Reduced merging times with balanced reward factors

Lane merging as a non-zero-sum game

Encourages further research on collaborative algorithms

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.