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Curse of rarity for autonomous vehicles

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  • Henry X. Liu

    (University of Michigan
    University of Michigan)

  • Shuo Feng

    (Tsinghua University)

Abstract

The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—presents significant challenges in ensuring the safety of autonomous vehicles using deep learning. Looking at it from distinct perspectives, we identify three potential approaches for addressing the issue.

Suggested Citation

  • Henry X. Liu & Shuo Feng, 2024. "Curse of rarity for autonomous vehicles," Nature Communications, Nature, vol. 15(1), pages 1-5, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49194-0
    DOI: 10.1038/s41467-024-49194-0
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    References listed on IDEAS

    as
    1. Shuo Feng & Haowei Sun & Xintao Yan & Haojie Zhu & Zhengxia Zou & Shengyin Shen & Henry X. Liu, 2023. "Dense reinforcement learning for safety validation of autonomous vehicles," Nature, Nature, vol. 615(7953), pages 620-627, March.
    2. Shuo Feng & Xintao Yan & Haowei Sun & Yiheng Feng & Henry X. Liu, 2021. "Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Kalra, Nidhi & Paddock, Susan M., 2016. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 182-193.
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