Abstract
This paper presents a sliding mode-based adaptive fault detection and emergency control algorithm for implementation in fail-safe systems of autonomous vehicles. The overall algorithm is comprised of a fault detection part and a fail-safe control part. For the former, sliding mode observer-based fault detection algorithms were developed for environment and chassis sensors, including LiDAR, Radar, and acceleration sensors. Unidentified fault signals from the sensors are reconstructed through the adaptive sliding mode observer. The reconstruction is based on the MIT rule through the use of an estimated sensitivity parameter. For the latter, a sliding mode control (SMC)-based emergency control method designed to respond to fault occurrences has been proposed to ensure the functional safety of autonomous vehicles. An adaptive gain parameter was designed, taking convergence time into consideration, to secure consistent and rapid responses from the controller. When the detection algorithm detects a fault, the appropriate control input is computed by a lower controller for the vehicle. This control input is calculated based on the last scene information obtained from an upper controller. The performance of the proposed fault detection and control algorithms has been evaluated through simulations and actual vehicle tests of various scenarios.
Highlights
There is no contest regarding the safety of autonomous vehicles being of the utmost importance within the industry
A thorough review of the studies mentioned above has shown that aspects of fail-safe systems such as fault detection and reconstruction have been studied through several methods, including observer-approaches, statistical methods, sensor monitoring, and artificial networks
An MIT rule-based adaptation rule to determine the magnitude of the observer injection term was proposed for the reconstruction of unknown faults
Summary
There is no contest regarding the safety of autonomous vehicles being of the utmost importance within the industry. Other areas of studies apart from autonomous vehicles have shown progress in fail-safe systems through the use of fault diagnosis and countermeasure methods for system malfunction or performance degradation. Fault detection and diagnosis methods have been used in fail-safe structures to develop the stability and reliability of their failsafe systems. Algorithms designed to diagnose faults in functional parts of autonomous vehicles were developed for fail-safe systems [48-51]. A thorough review of the studies mentioned above has shown that aspects of fail-safe systems such as fault detection and reconstruction have been studied through several methods, including observer-approaches, statistical methods, sensor monitoring, and artificial networks. 2) An MIT rule-based adaptive SMO methodology has been proposed for the detection of longitudinal faults in chassis and environment sensors (lidar, radar) and for the reconstruction of unknown faults in real driving scenarios.
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