Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey
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 passen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/18/3233 |
| _version_ | 1797485231214690304 |
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| author | Izaz Raouf Asif Khan Salman Khalid Muhammad Sohail Muhammad Muzammil Azad Heung Soo Kim |
| author_facet | Izaz Raouf Asif Khan Salman Khalid Muhammad Sohail Muhammad Muzammil Azad Heung Soo Kim |
| author_sort | Izaz Raouf |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:16:50Z |
| format | Article |
| id | doaj.art-a04df8c4205943ea8f04cb39656c9fb5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:16:50Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a04df8c4205943ea8f04cb39656c9fb52023-11-23T17:34:58ZengMDPI AGMathematics2227-73902022-09-011018323310.3390/math10183233Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent SurveyIzaz Raouf0Asif Khan1Salman Khalid2Muhammad Sohail3Muhammad Muzammil Azad4Heung Soo Kim5Department of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaFaculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering and Science and Technology, Topi, Swabi 23460, Khyber Pakhtunkhwa, PakistanDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University–Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaRecently, 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.https://www.mdpi.com/2227-7390/10/18/3233autonomous vehicleprognostic health managementsensor-based fault detectiondata-driven approachesperception sensors |
| spellingShingle | Izaz Raouf Asif Khan Salman Khalid Muhammad Sohail Muhammad Muzammil Azad Heung Soo Kim Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey Mathematics autonomous vehicle prognostic health management sensor-based fault detection data-driven approaches perception sensors |
| title | Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey |
| title_full | Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey |
| title_fullStr | Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey |
| title_full_unstemmed | Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey |
| title_short | Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey |
| title_sort | sensor based prognostic health management of advanced driver assistance system for autonomous vehicles a recent survey |
| topic | autonomous vehicle prognostic health management sensor-based fault detection data-driven approaches perception sensors |
| url | https://www.mdpi.com/2227-7390/10/18/3233 |
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