Abstract

Autonomous vehicles are expected to completely change the development model of the transportation industry and bring great convenience to our lives. Autonomous vehicles need to constantly obtain the motion status information with on-board sensors in order to formulate reasonable motion control strategies. Therefore, abnormal sensor readings or vehicle sensor failures can cause devastating consequences and can lead to fatal vehicle accidents. Hence, research on the fault tolerant control method is critical for autonomous vehicles. In this paper, we develop a robust fault tolerant path tracking control algorithm through combining the adaptive model predictive control algorithm for lateral path tracking control, improved weight assignment method for multi-sensor data fusion and fault isolation, and novel federal Kalman filtering approach with two states chi-square detector and residual chi-square detector for detection and identification of sensor fault in autonomous vehicles. Our numerical simulation and experiment demonstrate that the developed approach can detect fault signals and identify their sources with high accuracy and sensitivity. In the double line change path tracking control experiment, when the sensors failure occurs, the proposed method shows better robustness and effectiveness than the traditional methods. It is foreseeable that this research will contribute to the development of safer and more intelligent autonomous driving system, which in turn will promote the industrial development of intelligent transportation system.

Highlights

  • With the development of autonomous driving technology, autonomous vehicles have shown great value in improving vehicle safety, enhancing traffic efficiency, liberating the driver’s hands, etc., which has attracted more and more attention from relevant scholars [1]

  • In [4], the active disturbance rejection control method and differential flatness theory were used for lateral path tracking control of autonomous vehicle

  • We develop a fault-tolerant path tracking control approach with combining the model of adaptive model predictive control algorithm and a novel sensor fault detection method based on federal Kalman filtering and chi-square detector

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Summary

Introduction

With the development of autonomous driving technology, autonomous vehicles have shown great value in improving vehicle safety, enhancing traffic efficiency, liberating the driver’s hands, etc., which has attracted more and more attention from relevant scholars [1]. The large-scale implementation of autonomous vehicles in transportation systems require the effective sensor fault detection and robust path tracking motion control. We develop a fault-tolerant path tracking control approach with combining the model of adaptive model predictive control algorithm and a novel sensor fault detection method based on federal Kalman filtering and chi-square detector. The main contributions of this work can be described as: (1) a single-track 3DOF vehicle dynamics model is established and Taylor expansion is performed for modelling linearization; (2) an adaptive model predictive control algorithm is developed for robust path tracking control; (3) an improved weight assignment method based data fusion method is proposed for multi-sensor data fusion and fault signal isolation; and (4) a novel sensor fault detection approach is proposed using federal Kalman filtering and fault detector with parallel structure for each sensing source, consisting of two state chi-square detector and residual chi-square detector.

Methodology
Modeling and Problem Formulation
Schematic
Linearization of Vehicle Dynamics Model
Construct the Constraints
Dynamic Constraint of Tire Cornering
Lateral Acceleration Constraints
Construct the Objective Function
Adaptive Model Predictive Controller for Vehicle Lateral Motion Control
Merging Multi-Sensor Data and Isolating Fault Signals
Fault Signal Detector Design
Simulation Verification
Experimental Verification
Conclusions
Full Text
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