Abstract: Embodiments and examples are disclosed for intelligent vehicle diagnostics using driving behavior modeling and monitoring. For one example, a data processing system for a vehicle includes a plurality of sensors and a vehicle control unit (VCU). The VCU may sample the output from each of the plurality of sensors and assemble a dataset which may be transmitted to a cloud computing center. The cloud computing center may apply statistical machine learning algorithms to the dataset and training data to develop a model of a user's expected driving behavior. The cloud computing center may transmit the model to the vehicle, wherein the VCU may utilize the model to monitor the user's driving behavior. In response to detecting driving behavior that is anomalous to the expected driving behavior, the VCU may diagnose the cause of the anomalous behavior and take one or more preventative actions based on the diagnosis.

Abstract: Embodiments and examples are disclosed for intelligent vehicle diagnostics using driving behavior modeling and monitoring. For one example, a data processing system for a vehicle includes a plurality of sensors and a vehicle control unit (VCU). The VCU may sample the output from each of the plurality of sensors and assemble a dataset which may be transmitted to a cloud computing center. The cloud computing center may apply statistical machine learning algorithms to the dataset and training data to develop a model of a user's expected driving behavior. The cloud computing center may transmit the model to the vehicle, wherein the VCU may utilize the model to monitor the user's driving behavior. In response to detecting driving behavior that is anomalous to the expected driving behavior, the VCU may diagnose the cause of the anomalous behavior and take one or more preventative actions based on the diagnosis.

Abstract: Embodiments and examples are disclosed for intelligent detection of vehicle intrusion using command pattern modeling and analysis. For one example, a vehicle control unit (VCU) may monitor, using a model of a user's expected driving behavior, the user's current driving behavior and detect anomalous driving behavior based on the model. The VCU may determine that the anomalous driving behavior does not correspond to one or more commands on a network bus of the vehicle and analyze, using a command pattern model, a pattern among the one or more commands on the network bus. The VCU may then compare, using the command pattern model, the pattern among the one or more commands to a historical command pattern to determine if an intrusion (e.g., by a hacker) is taking place.