Functional perspective-based probabilistic fault detection and diagnostic algorithm for autonomous vehicle using longitudinal kinematic model
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This paper describes a functional perspective-based probabilistic fault detection and diagnostic algorithm of an autonomous vehicle using a longitudinal kinematic model. The relative displacement and velocity between the subject vehicle and a preceding vehicle was obtained by a radar installed in front of the autonomous vehicle. The obtained relative values were used to control the longitudinal behavior of the autonomous vehicle, the longitudinal acceleration of which was obtained from an internal sensor. In order to detect and diagnose actual faults in the obtained values, such as relative displacement, velocity, and acceleration, a fault detection and diagnostic algorithm for the longitudinal control of the autonomous vehicle was developed using a sliding mode observer and predictive function. The probabilistic analysis of fault signals was conducted using the constructed sliding mode observer and predictive function. The actual driving data of the vehicle preceding the subject vehicle was used for the rational performance evaluation of the proposed algorithm. The performance evaluation was conducted in the MATLAB/SIMULINK environment. The evaluation results showed that the proposed fault detection and diagnostic algorithm can stochastically detect and diagnose applied fault signals.
This work was supported by a grant from the National Research Foundation (NRF) of Korea, funded by the Ministry of Science, ICT, and Future Planning (MSIP) (No. NRF-2016R1E1A1A01943543).
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