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A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

Author

Listed:
  • Xiaoyan Yu

    (Mechanical and Systems Engineering School, Newcastle University, Newcastle upon Tyne NE1 7RU, UK)

  • Marin Marinov

    (Engineering Systems and Management (ESM), Aston University, Birmingham B4 7ET, UK)

Abstract

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed.

Suggested Citation

  • Xiaoyan Yu & Marin Marinov, 2020. "A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-26, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3281-:d:346903
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    References listed on IDEAS

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    1. Dongyong Zhang & Junjuan Liu & Bingjun Li, 2014. "Tackling Air Pollution in China—What do We Learn from the Great Smog of 1950s in LONDON," Sustainability, MDPI, vol. 6(8), pages 1-17, August.
    2. Ke Wang & Yingnan Liu, 2014. "Can Beijing fight with haze? Lessons can be learned from London and Los Angeles," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 1265-1274, June.
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    Cited by:

    1. Yao-Liang Chung, 2023. "Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes," Future Internet, MDPI, vol. 15(10), pages 1-26, September.
    2. Ángel Valera & Francisco Valero & Marina Vallés & Antonio Besa & Vicente Mata & Carlos Llopis-Albert, 2021. "Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-21, January.

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