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Rule-Based Constrained Deep Reinforcement Learning for Intelligent Vehicle Driving Decisions in Highway Scenarios
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Xinkai Wang1, 2, Shufeng Wang1, Shihao Wang1, 2
Automobile Technology | 2023, (9) : 18 - 26
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Automobile Technology | 2023, (9): 18-26
Rule-Based Constrained Deep Reinforcement Learning for Intelligent Vehicle Driving Decisions in Highway Scenarios
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Xinkai Wang1, 2, Shufeng Wang1, Shihao Wang1, 2
Affiliations
  • 1 Shandong University of Science and Technology, Qingdao 266590
  • 2 Shandong Wuzheng Group Co., Ltd., Rizhao 262306
Published: 2023-09-24 doi: 10.19620/j.cnki.1000-3703.20220752
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For the problems of strong randomness and low training efficiency in intelligent vehicle training under reinforcement learning algorithm, this paper proposed a driving decision framework of intelligent vehicle based on rule constraints and Deep Q Network (DQN) algorithm. The introduced rules were divided into hard constraints related to lane change and soft constraints related to lane keeping, which were implemented by Action Detection Module and reward function respectively. At the same time, the network structure of DQN was improved by combining Dueling DQN and Double DQN, N-Step Bootstrapping learning was introduced to accelerate the training efficiency of DQN. Finally, the effectiveness of the model was verified by comprehensive comparison with the original DQN algorithm in the highway scene of Highway-env platform. The improved algorithm improved the task success rate and training efficiency of intelligent vehicles.

Intensive learning  /  Driving decision  /  Intelligent vehicle  /  Rule constraint  /  Improved DQN algorithm
Xinkai Wang, Shufeng Wang, Shihao Wang. Rule-Based Constrained Deep Reinforcement Learning for Intelligent Vehicle Driving Decisions in Highway Scenarios[J]. Automobile Technology, 2023 , (9) : 18 -26 . DOI: 10.19620/j.cnki.1000-3703.20220752
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Article Info
doi: 10.19620/j.cnki.1000-3703.20220752
  • Online Date：2025-12-07
  • Published：2023-09-24
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  • Revised：2022-11-17
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    1 Shandong University of Science and Technology, Qingdao 266590
    2 Shandong Wuzheng Group Co., Ltd., Rizhao 262306
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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