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Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey

Author

Listed:
  • Izaz Raouf

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Asif Khan

    (Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering and Science and Technology, Topi, Swabi 23460, Khyber Pakhtunkhwa, Pakistan)

  • Salman Khalid

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Muhammad Sohail

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Muhammad Muzammil Azad

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

Abstract

Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.

Suggested Citation

  • Izaz Raouf & Asif Khan & Salman Khalid & Muhammad Sohail & Muhammad Muzammil Azad & Heung Soo Kim, 2022. "Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey," Mathematics, MDPI, vol. 10(18), pages 1-26, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3233-:d:907968
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    References listed on IDEAS

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    Cited by:

    1. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    2. 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.
    3. Lili Zheng & Yanlin Zhang & Tongqiang Ding & Fanyun Meng & Yanlin Li & Shiyu Cao, 2022. "Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks," Mathematics, MDPI, vol. 10(24), pages 1-23, December.
    4. Izaz Raouf & Prashant Kumar & Hyewon Lee & Heung Soo Kim, 2023. "Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System," Mathematics, MDPI, vol. 11(4), pages 1-14, February.

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