IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i7d10.1007_s10845-022-01983-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-01983-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-022-01983-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Bertolini & Massimo Riccaboni, 2021. "Grounding the case for a European approach to the regulation of automated driving: the technology-selection effect of liability rules," European Journal of Law and Economics, Springer, vol. 51(2), pages 243-284, April.
    2. Henry X. Liu & Shuo Feng, 2024. "Curse of rarity for autonomous vehicles," Nature Communications, Nature, vol. 15(1), pages 1-5, December.
    3. Zoltan Ferenc Magosi & Christoph Wellershaus & Viktor Roland Tihanyi & Patrick Luley & Arno Eichberger, 2022. "Evaluation Methodology for Physical Radar Perception Sensor Models Based on On-Road Measurements for the Testing and Validation of Automated Driving," Energies, MDPI, vol. 15(7), pages 1-20, March.
    4. Hudson, John & Orviska, Marta & Hunady, Jan, 2019. "People’s attitudes to autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 164-176.
    5. Liu, Peng & Zhang, Yawen & He, Zhen, 2019. "The effect of population age on the acceptable safety of self-driving vehicles," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 341-347.
    6. Cian Ryan & Finbarr Murphy & Martin Mullins, 2019. "Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1125-1140, May.
    7. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    8. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    9. Scott Le Vine & You Kong & Xiaobo Liu & John Polak, 2019. "Vehicle automation and freeway ‘pipeline’ capacity in the context of legal standards of care," Transportation, Springer, vol. 46(4), pages 1215-1244, August.
    10. Mohamed Alawadhi & Jumah Almazrouie & Mohammed Kamil & Khalil Abdelrazek Khalil, 0. "Review and analysis of the importance of autonomous vehicles liability: a systematic literature review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-23.
    11. Makoto Fujiu & Yuma Morisaki & Jyunich Takayama, 2024. "Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator," Sustainability, MDPI, vol. 16(2), pages 1-15, January.
    12. Du, Manqing & Zhang, Tingru & Liu, Jinting & Xu, Zhigang & Liu, Peng, 2022. "Rumors in the air? Exploring public misconceptions about automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 237-252.
    13. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    14. Mohamed Alawadhi & Jumah Almazrouie & Mohammed Kamil & Khalil Abdelrazek Khalil, 2020. "Review and analysis of the importance of autonomous vehicles liability: a systematic literature review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1227-1249, December.
    15. Hussain, Qinaat & Alhajyaseen, Wael K.M. & Adnan, Muhammad & Almallah, Mustafa & Almukdad, Abdulkarim & Alqaradawi, Mohammed, 2021. "Autonomous vehicles between anticipation and apprehension: Investigations through safety and security perceptions," Transport Policy, Elsevier, vol. 110(C), pages 440-451.
    16. Ljubi, Klara & Groznik, Aleš, 2023. "Role played by social factors and privacy concerns in autonomous vehicle adoption," Transport Policy, Elsevier, vol. 132(C), pages 1-15.
    17. Mohamed Abdel-Aty & Shengxuan Ding, 2024. "A matched case-control analysis of autonomous vs human-driven vehicle accidents," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Fabian Pütz & Finbarr Murphy & Martin Mullins, 2019. "Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk," Environment Systems and Decisions, Springer, vol. 39(4), pages 383-395, December.
    19. Yilun Chen & Nirajan Shiwakoti & Peter Stasinopoulos & Shah Khalid Khan, 2022. "State-of-the-Art of Factors Affecting the Adoption of Automated Vehicles," Sustainability, MDPI, vol. 14(11), pages 1-29, May.
    20. Yu Lin & Hongfei Jia & Bo Zou & Hongzhi Miao & Ruiyi Wu & Jingjing Tian & Guanfeng Wang, 2021. "Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes," Sustainability, MDPI, vol. 13(6), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:7:d:10.1007_s10845-022-01983-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.