Real-Time Kinematics-Based Sensor-Fault Detection for Autonomous Vehicles Using Single and Double Transport with Adaptive Numerical Differentiation
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912265955966976 |
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| author | Verma, Shashank Bernstein, Dennis S. |
| author_facet | Verma, Shashank Bernstein, Dennis S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05158 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-Time Kinematics-Based Sensor-Fault Detection for Autonomous Vehicles Using Single and Double Transport with Adaptive Numerical Differentiation Verma, Shashank Bernstein, Dennis S. Systems and Control Signal Processing 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. |
| title | Real-Time Kinematics-Based Sensor-Fault Detection for Autonomous Vehicles Using Single and Double Transport with Adaptive Numerical Differentiation |
| topic | Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2309.05158 |