Abstract: The present invention relates to an apparatus and method for monitoring abnormal state of a vehicle. In the method, CAN data collected from an ECU mounted on the vehicle is transformed into coordinates. The coordinates are applied to a distribution map in a specific space, a number of clusters is calculated based on results of the application, and an initial center point corresponding to the number of clusters is selected. Clustering is performed based on the initial center point, and then clusters are generated. At least one piece of data is extracted from each of the clusters, and a state feature of a corresponding cluster is decided on using a difference between maximum and minimum values of attributes constituting the at least one piece of data. A current state of the vehicle is monitored based on current CAN data of the vehicle and state features of the clusters.

Abstract: The present invention relates to an apparatus and method for monitoring abnormal state of a vehicle. In the method, CAN data collected from an ECU mounted on the vehicle is transformed into coordinates. The coordinates are applied to a distribution map in a specific space, a number of clusters is calculated based on results of the application, and an initial center point corresponding to the number of clusters is selected. Clustering is performed based on the initial center point, and then clusters are generated. At least one piece of data is extracted from each of the clusters, and a state feature of a corresponding cluster is decided on using a difference between maximum and minimum values of attributes constituting the at least one piece of data. A current state of the vehicle is monitored based on current CAN data of the vehicle and state features of the clusters.

Abstract: The apparatus includes a data normalization unit, a neural network problem prediction unit, and a transition change prediction unit. The data normalization unit creates normalization transformation values by performing normalization transformation based on threshold value ranges for a plurality of pieces of vehicle network data. The neural network problem prediction unit creates a neural network problem prediction value by predicting a mixed problem with the vehicle using a multi-artificial neural network model, created based on a learning data set related to mixed problems having previously occurred in the vehicle and the normalization transformation values. The transition change prediction unit predicts a change in transition for the mixed problem according to a change in the neural network problem prediction value, by analyzing the neural network problem prediction value and previous neural network problem prediction values previously created in the vehicle.