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Testing the reliability of monocular obstacle detection methods in a simulated 3D factory environment

Author

Listed:
  • Marius Wenning

    (Werkzeugmaschinenlabor, RWTH Aachen University)

  • Anton Akira Backhaus

    (Werkzeugmaschinenlabor, RWTH Aachen University)

  • Tobias Adlon

    (Werkzeugmaschinenlabor, RWTH Aachen University)

  • Peter Burggräf

    (Werkzeugmaschinenlabor, RWTH Aachen University)

Abstract

Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms hypothetically allow the replacement of distance measuring laser scanners with common monocameras. In this paper, we present a photorealistic 3D simulated factory environment for testing vision-based obstacle detecting algorithms preceding field tests on the safety–critical system. We further test two obstacle detection methods employing state-of-the-art semantic segmentation and depth estimation in a range of challenging test scenarios. Both models performed well under common factory settings. Some edge cases, however, lead to vehicle crashes.

Suggested Citation

  • Marius Wenning & Anton Akira Backhaus & Tobias Adlon & Peter Burggräf, 2022. "Testing the reliability of monocular obstacle detection methods in a simulated 3D factory environment," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2157-2165, October.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:7:d:10.1007_s10845-022-01983-4
    DOI: 10.1007/s10845-022-01983-4
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    References listed on IDEAS

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    1. 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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