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Intelligent-Assist Algorithm for Remote Shared-Control Driving Based on Game Theory

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Abstract

Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps. For unknown environments or scenarios where perception fails, a human-in-the-loop remote-driving system can effectively complement common solutions, although safety remains an issue for its application. A haptic shared-control algorithm based on non-cooperative game theory is presented in this paper. The algorithm generates collision-free reference paths with model predictive control and predicts the driver’s path using a two-point preview model. Man-machine torque interaction is modeled as a Nash game, and the assist system’s degree of intervention is regulated in real time, according to assessments of collision risk and the driver’s concentration. Simulations of several representative scenarios demonstrate how the proposed method improves driving safety, while respecting driver decisions.

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Acknowledgment

The authors would like to thank the university and this project’s collaborating automobile manufacturer for providing the required equipment and software for our experiments.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Bangjun Qiao  (乔邦峻), Huanghe Li  (李黄河) & Xiaodong Wu  (吴晓东)

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  1. Bangjun Qiao  (乔邦峻)
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  2. Huanghe Li  (李黄河)
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  3. Xiaodong Wu  (吴晓东)
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Corresponding author

Correspondence to Bangjun Qiao  (乔邦峻).

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the National Natural Science Foundation of China (No. 51775331)

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Qiao, B., Li, H. & Wu, X. Intelligent-Assist Algorithm for Remote Shared-Control Driving Based on Game Theory. J. Shanghai Jiaotong Univ. (Sci.) 26, 615–625 (2021). https://doi.org/10.1007/s12204-021-2351-z

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  • DOI: https://doi.org/10.1007/s12204-021-2351-z

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