Diagnosis of autonomous vehicles using machine learning

With autonomous trucks on the road where the driver is absent requires new diagnostic methods. The driver possess several abilities which a machine does not. In this thesis, the use of machine learning as a method was investigated. A more concrete problem description was formed where the main objective was detecting anomalies in wheel configurations. More specifically, the machine learning model was used to detect incorrect wheel settings. Three different algorithms was used, SVM, LDA and logistic regression. Overall, the classifier predicts with high accuracy supporting that machine learning can be used for diagnosing autonomous vehicles