Dual-sliding mode approach for separated fault detection and tolerant control for functional safety of longitudinal autonomous driving

Abstract

This paper describes model-based separated fault detection and fault tolerant control of longitudinal autonomous driving using dual-sliding mode observer for functional safety. Internal and environment sensors such as camera or radar are required to measure the acceleration information of the subject vehicle and the relative distance and velocity information between the preceding and subject vehicles in longitudinal autonomous driving. In order to detect the independent fault of each sensor, a dual-sliding mode observer (SMO) is used for fault reconstruction under the assumption that V2V (Vehicle to Vehicle) communication for vehicle driving state is available. The each SMO reconstructs the expected fault in sensor based on discontinuous injection term used for converging output error to zero. Based on the reconstructed fault by each SMO, faults are detected using threshold approach. When the fault is detected, the reconstructed fault is used for fault tolerant control by subtracting to faulty data. The proposed fault detection (FD) and fault tolerant control (FTC) algorithms were evaluated using actual driving data and a three-dimensional (3D) vehicle model with a linear quadratic regulator for following control. The evaluation results are presented and analyzed with regard to fault reconstruction, detection, and tolerant control in four cases wherein two types of faults were applied.

Adaptive Fault Detection and Emergency Control of Autonomous Vehicles for Fail-Safe Systems Using a Sliding Mode Approach

“…In Table II, some results are not depicted in this paper. Some results [15], [40], [41] are not in this paper but get similar results as shown here. The omitted graphs are replaced by the resulting graph in the reference paper.…”

“…A fail-safe priority-based intersection approach was developed to manage signal-less intersections autonomously [46]. A fault detection, isolation, and identification architecture for multi-faults in multi-sensor systems has been introduced in [47]. Algorithms designed to diagnose faults in functional parts of autonomous vehicles were developed for fail-safe systems [48][49][50][51].…”

Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches

“…The red regions of Fig. 2 show the module tested by our research team [13,14,15,16,22,23]. The blue region indicates an algorithm, a Lidar point cloud de-noising for adverse weather.…”

“…A fault detection, isolation, and identification architecture for multi-faults in multi-sensor systems has been introduced in [12]. Functional perspective-based fault detection and a diagnostic algorithm were proposed for autonomous vehicle fail-safe systems in [13][14][15][16]. This research focuses on LiDAR, Radar, and vehicle chassis sensor faults.…”

“…These were then validated through simulations and vehicle tests. Following fault detection, fault-tolerant controls and safety controls with limited information were also proposed in [13,14,17,21,22,23]. Some studies suggested the utilization of environmental sensors in autonomous driving to correct the error information of other sensors.…”