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

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.

Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking

“…However, the fault detection rate was not measured [ 22 ]. A novel active fault tolerant control scheme was developed for handling component/actuator faults [ 1 ]. A new adaptive event-triggered mechanism was discussed for obtaining a suitable compromise between control performance and real-time transmission consumption [ 23 ].…”

“…AVs have an electronic system that performs the driving operations. AVs have vital features for the security of current vehicles [ 1 ]. With increasing demands for secure and quick transportation services for life-saving medical devices, there is a growing emphasis on safety, reliability, sustainability, and steadfastness, which makes it an extremely analytical and active study area among control communities.…”

A Predictive Model of a Driver’s Target Trajectory Based on Estimated Driving Behaviors

“…As a result, the Advanced Driver Assistance System (ADAS) has become a promising system for reducing driving workload and helping drivers deal with complex situations. One classic function of an ADAS is the Lane-Keeping Assist System (LKAS), which can prevent unintentional lane departure by alert or vibration [ 5 , 6 ]. Some researchers also explored its application in assisting drivers to keep or change lanes [ 7 , 8 ].…”