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Future vehicles: learnable wheeled robots

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Abstract

As one of the important signs of the third wave of artificial intelligence, wheeled robots not only inherit knowledge but also learn independently, which brings about to learnable wheeled robots that use a driving brain to achieve data-driven control and learning. Presently, most existing technologies for self-driving vehicles can learn positively from the benchmark drivers to guarantee safe driving. However, in many unpredicted situations, such as rollover, human drivers often cause the behavior of irrational subconscious on account of human emotions like panic. In this paper, we propose a learnable wheeled robot using the driving brain by taking the rollover as an example, which is the most serious and dangerous situation in dynamic vehicle operations. Then, based on the analysis of rollover accidents, we utilize the driving brain reversely and conduct negative learning, materializing, and condensing the group intelligence of accident experts, to solve the problem of the lack of individual intelligence in emergencies and further promote real-time response to other dangerous conditions, such as puncture for self-driving vehicles.

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Acknowledgements

This work was partially founded by National Natural Science Foundation of China (Grant Nos. 61871038, 61931012), Beijing Natural Science Foundation (Grant No. 4182022), and Major project of Beijing Social Science Foundation (Grant No. 18ZDA09). We would like to thank Zhixuan WU from college of Robotics, Beijing Union University and Yang XU and Yan WANG from college of Software, Tsinghua University for the help on this work. We really thank anonymous reviewer’s constructive suggestions.

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Authors and Affiliations

  1. Academy of Military Science, Beijing, 100036, China

    Deyi Li

  2. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

    Nan Ma

  3. College of Robotics, Beijing Union University, Beijing, 100101, China

    Nan Ma

  4. School of Software, Tsinghua University, Beijing, 100084, China

    Yue Gao

Authors
  1. Deyi Li
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  2. Nan Ma
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  3. Yue Gao
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Correspondence to Nan Ma.

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Li, D., Ma, N. & Gao, Y. Future vehicles: learnable wheeled robots. Sci. China Inf. Sci. 63, 193201 (2020). https://doi.org/10.1007/s11432-019-2787-2

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  • DOI: https://doi.org/10.1007/s11432-019-2787-2

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