A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 10
Oct.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
T. Y. K. Zhang, J. X. Zhan, J. M. Shi, J. M. Xin, and  N. N. Zheng,  “Human-like decision-making of autonomous vehicles in dynamic traffic scenarios,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1905–1917, Oct. 2023. doi: 10.1109/JAS.2023.123696
Citation: T. Y. K. Zhang, J. X. Zhan, J. M. Shi, J. M. Xin, and  N. N. Zheng,  “Human-like decision-making of autonomous vehicles in dynamic traffic scenarios,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1905–1917, Oct. 2023. doi: 10.1109/JAS.2023.123696

Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios

doi: 10.1109/JAS.2023.123696
Funds:  This work was supported by the National Key R&D Program of China (2022YFB2502900) and the National Natural Science Foundation of China (62088102, 61790563)
More Information
  • With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety. Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms; 2) The driving datasets and simulation platforms for testing and verifying human-like decision-making; 3) The evaluation metrics of human-likeness; personalized driving; the application of decision-making in real traffic scenarios; and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.

     

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    Highlights

    • In recent years, there have been many works on autonomous driving decision-making. However, there hasn't been a comprehensive review from a "human-like" perspective. This paper is the first review that provides a comprehensive overview of human-like decision-making for autonomous vehicles
    • In this paper, some original issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms; 2) The driving datasets and simulation platforms for testing and verifying human-like decision-making; 3) The evaluation metrics of human-likeness; personalized driving; the application of decision-making in real traffic scenarios; and 4) The potential research direction of human-like driving
    • It is very important to improve the ability of the decision-making system to build a reasonable driving model so that the autonomous vehicle can learn human expert knowledge and driving habits. The research results of this paper are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios

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