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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Kexuan Lv & Xiaofei Pei & Ci Chen & Jie Xu, 2022. "A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning," Mathematics, MDPI, vol. 10(9), pages 1-24, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1551-:d:808743
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    References listed on IDEAS

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    1. Unknown, 2016. "Proceedings Of Abstracts," 152nd Seminar, August 30 - September 1, 2016, Novi Sad, Serbia 244068, European Association of Agricultural Economists.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Bowen Gong & Zhipeng Xu & Ruixin Wei & Tao Wang & Ciyun Lin & Peng Gao, 2023. "Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    2. Zeyang Lin & Jun Lai & Xiliang Chen & Lei Cao & Jun Wang, 2022. "Learning to Utilize Curiosity: A New Approach of Automatic Curriculum Learning for Deep RL," Mathematics, MDPI, vol. 10(14), pages 1-20, July.

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