A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning

Kexuan Lv, Xiaofei Pei, Ci Chen and Jie Xu
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Kexuan Lv: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Xiaofei Pei: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Ci Chen: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jie Xu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2022, vol. 10, issue 9, 1-24

Abstract: As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a prominent role in the decision-making process of autonomous driving (AD), which enables autonomous vehicles (AVs) to learn an optimal driving strategy through continuous interaction with the environment. This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy for AD tasks in the highway scenarios where an AV merges into two-lane road traffic flow and realizes the lane changing (LC) maneuvers. We integrate the DRL model into the AD system relying on the end-to-end learning method. An improved DRL algorithm based on deep deterministic policy gradient (DDPG) is developed with well-defined reward functions. In particular, safety rules (SR), safety prediction (SP) module and trauma memory (TM) as well as the dynamic potential-based reward shaping (DPBRS) function are adopted to further enhance safety and accelerate learning of the LC behavior. For validation, the proposed DSSTD algorithm is trained and tested on the dual-computer co-simulation platform. The comparative experimental results show that our proposal outperforms other benchmark algorithms in both driving safety and efficiency.

Keywords: autonomous driving; decision-making; lane changing; reinforcement learning; DDPG; safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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