Real-Time Kinematics-Based Sensor-Fault Detection for Autonomous Vehicles Using Single and Double Transport with Adaptive Numerical Differentiation

TL;DR

This paper presents a model-independent, real-time sensor fault detection method for autonomous vehicles using kinematic relations and adaptive numerical differentiation, applicable to ground and aerial vehicles.

Contribution

Introduces KSFD, a novel sensor fault detection approach that relies solely on sensor data and kinematic relations, eliminating the need for models or prior knowledge.

Findings

Successfully detects single sensor faults in real time

Applicable to both ground and aerial vehicles

Validated through simulated and experimental tests

Abstract

Sensor-fault detection is crucial for the safe operation of autonomous vehicles. This paper introduces a novel kinematics-based approach for detecting and identifying faulty sensors, which is model-independent, rule-free, and applicable to ground and aerial vehicles. This method, called kinematics-based sensor fault detection (KSFD), relies on kinematic relations, sensor measurements, and real-time single and double numerical differentiation. Using onboard data from radar, rate gyros, magnetometers, and accelerometers, KSFD uniquely identifies a single faulty sensor in real time. To achieve this, adaptive input and state estimation (AISE) is used for real-time single and double numerical differentiation of the sensor data, and the single and double transport theorems are used to evaluate the consistency of data. Unlike model-based and knowledge-based methods, KSFD relies solely on sensor signals, kinematic relations, and AISE for real-time numerical differentiation. For ground vehicles, KSFD requires six kinematics-based error metrics, whereas, for aerial vehicles, nine error metrics are used. Simulated and experimental examples are provided to evaluate the effectiveness of KSFD.