A method of personalized driving decision for smart car based on deep reinforcement learning

Xinpeng Wang, Chaozhong Wu, Jie Xue, Zhijun Chen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
23 Downloads (Pure)


To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving.

Original languageEnglish
Article number295
JournalInformation (Switzerland)
Issue number6
Publication statusPublished - 2020


  • Data visualization
  • Deep reinforcement learning
  • Driving decision
  • Human-like
  • Personalization
  • Smart car


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