Performance Index Modeling from Fault Injection Analysis for an Autonomous Lane-Keeping System

A faulty sensor data could not only undermine the stability but also drastically compromise the safety of autonomous systems. The reliability of the functional operation can be significantly enhanced, if any monitoring modules can evaluate the risk on the system for a particular fault in a sensor. Based on the estimated risk, the system can then execute the necessary safety operation. To develop a risk evaluating algorithm, the relation between the faults and the effects should be known. Therefore, to establish such cause-and-effect relationship, this paper presents a performance indexing method that quantifies the effects caused by given fault types with different intensities. Here, the considered system is a lane keeping robot and the only sensor used for the functional operation is a red, green, and blue (RGB) camera. The lane keeping algorithm is modeled using a supervised artificial intelligence (AI) learning method. To quantify the effects with performance indices (PIs), different faults are injected to the RBG camera. For an injected fault type, the system's PI is evaluated from the AI algorithm's (open-loop) outcome and the lane keeping (closedloop) outcome. The lane keeping/closed-loop outcome is quantified from the trajectory data computed using the strapdown inertial navigation algorithm with the measurement data from a 6D inertial measurement unit (IMU).

Keywords: Fault injection, RGB camera fault, Performance index, IMU trajectory, Strapdown inertial navigation.

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