Automated advanced analytics on vehicle data using AI

Sammanfattning: The evolution of electrification and autonomous driving on automotive leads to the increasing complexity of the in-vehicle electrical network, which poses a new challenge for testers to do troubleshooting work in massive log files. This thesis project aims to develop a predictive technique for anomaly detection focusing on user function level failures using machine learning technologies.\\ Specifically, it investigates the performance of point anomaly detection models and temporal dependent anomaly detection models on the analysis of Controller Area Network (CAN) data obtained from software-in-loop simulation. For point anomaly detection, the models of Isolation forest, Multivariate normal distribution, and Local outlier factor are implemented respectively. For temporal dependent anomaly detection, the model of an encoder-decoder architecture neural network using Long Short-Temporal Memory (LSTM) units is implemented, so is a stacking hybrid detector in the combination of LSTM Encoder and Local outlier factor.\\ With a comparison of the comprehensive performance of the proposed models, the model of LSTM AutoEncoder is selected for detecting the anomalies on sequential data in CAN logs. The experiment results show promising detection performance of LSTM AutoEncoder on the studied functional failures and suggest that it is possible to be deployed in real-time automated anomaly detection on vehicle systems.