Abstract
Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.
Highlights
Autonomous driving has received significant research interest in the past two decades due to its many potential societal and economical benefits
To address the above issues, we develop a multi-agent reinforcement learning algorithm by employing a multiagent advantage actor-critic network (MA2C) for multiAV lane-changing decision making, featuring a novel local reward design that incorporates the safety, efficiency, and passenger comfort as well as a parameter sharing scheme to foster inter-agent collaborations
3 Problem formulation we review the preliminaries of reinforcement learning (RL) and formulate the considered highway lane-changing problem as a partially observable Markov decision process (POMDP)
Summary
Autonomous driving has received significant research interest in the past two decades due to its many potential societal and economical benefits. Autonomous vehicles (AVs) promise fewer emissions [1] but are expected to improve safety and efficiency. There exists a huge challenge in the task of high-level decision-making in AVs due to the complex and dynamic traffic environment, especially. The considered lane-changing scenario is illustrated, where AVs and HDVs co-exist on a one-way highway with two lanes. The AVs aim to safely travel through the traffic while making necessary lane changes to overtake slow-moving vehicles for improved efficiency. In the presence of multiple AVs, the AVs are expected to collaboratively learn a policy to adapt to HDVs and enable safe and efficient lane changes. As HDVs bring unknown/uncertain behaviors, planning, and control in such mixed traffic to realize safe and efficient maneuvers is a challenging task [4]
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