4.7 Article

Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2021.103489

关键词

Autonomous vehicle; Ride comfort; Energy efficiency; Deep reinforcement learning; Speed control

资金

  1. National Natural Science Foundation of China [52102383]
  2. China Postdoctoral Science Foundation [2021M692428]
  3. Innovation Program of Shanghai Municipal Education Commission [2021-01-07-00-07-E00092]
  4. Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]
  5. Shanghai Sailing Program [21YF1449400]

向作者/读者索取更多资源

Crowdsourced data can enhance driving performance of autonomous vehicles on rough pavements by controlling speed to address discomfort and inefficiency issues. The study introduces the concept of 'maximum comfortable speed' and designs a deep reinforcement learning algorithm to learn comfortable and energy-efficient speed control strategies.
Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing methods to address these problems are time-consuming and not adaptive to changing driving conditions on rough pavements. With the development of sensor and communication technologies, crowdsourced road and dynamic traffic information become available for enhancing driving performance, particularly addressing the discomfort and inefficiency issues by controlling driving speeds. This study proposes a speed control framework on rough pavements, envisioning the operation of autonomous vehicles based on the crowdsourced data. We suggest the concept of 'maximum comfortable speed' for representing the vertical ride comfort of oncoming roads. A deep reinforcement learning (DRL) algorithm is designed to learn comfortable and energyefficient speed control strategies. The DRL-based speed control model is trained using realworld rough pavement data in Shanghai, China. The experimental results show that the vertical ride comfort, energy efficiency, and computation efficiency increase by 8.22%, 24.37%, and 94.38%, respectively, compared to an optimization-based speed control model. The results indicate that the proposed framework is effective for real-time speed controls of autonomous vehicles on rough pavements.

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