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AUTOMATIC ROOT CAUSE ANALYSIS OF FAILURES IN AUTONOMOUS VEHICLE
AUTOMATIC ROOT CAUSE ANALYSIS OF FAILURES IN AUTONOMOUS VEHICLE
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机译：自动车辆故障自动根本原因分析
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摘要
Automatically detecting failure root cause in an autonomous vehicle, by receiving sensor data captured during a period preceding the failure by sensor(s) deployed to sense an environment of the autonomous vehicle, analyzing the sensor data to identify object(s) in the environment, creating a failure scenario defining a time-lined motion pattern of each object, computing a feature vector comprising features extracted from an output generated by sub-systems of the autonomous vehicle during the failure scenario, applying to the feature vector machine learning classification model(s) trained with a plurality of labeled feature vectors computed for a plurality of failure scenarios and their corresponding success scenarios, identifying key features significantly contributing to an outcome of the trained machine learning classification model(s) by applying an interpretation model to the machine learning classification model(s), the feature vector(s) and/or the outcome and estimating root cause failure sub-system(s) according to its association with the key feature(s).
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