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

Izaz Raouf, Asif Khan, Salman Khalid, Muhammad Sohail, Muhammad Muzammil Azad and Heung Soo Kim ()
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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

Mathematics, 2022, vol. 10, issue 18, 1-26

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.

Keywords: autonomous vehicle; prognostic health management; sensor-based fault detection; data-driven approaches; perception sensors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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