期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 10, 页码 18808-18821出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3160936
关键词
Planning; Autonomous vehicles; Safety; Merging; Predictive models; Navigation; Games; Deep reinforcement learning; dense traffic; motion planning; safe learning; trajectory optimization
资金
- Netherlands Organisation for Scientific Research (NWO) Domain Applied Sciences (Veni) [15916, NWA.1292.19.298]
- Amsterdam Institute for Advanced Metropolitan Solutions
This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. By combining deep reinforcement learning and optimization-based planning, the proposed method significantly reduces collisions and increases the success rate in interactive simulation environments.
Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model) and exploit this reasoning to navigate through dense traffic safely. This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We explore the connection between human driving behavior and their velocity changes when interacting. Hence, we propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles to an optimization-based planner ensuring safety and kinematic feasibility through constraint satisfaction. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case other vehicles do not yield. We present qualitative and quantitative results in highly interactive simulation environments (highway merging and unprotected left turns) against two baseline approaches, a learning-based and an optimization-based method. The presented results show that our method significantly reduces the number of collisions and increases the success rate with respect to both learning-based and optimization-based baselines.
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