Disclosed herein is a vehicle theft prevention device. The device can include a data store including event configuration data. The device can include one or more sensors that can sense various types of measurements proximate to a vehicle. The device can include a computing device in communication with the sensors. The computing device can read measurements from the sensors and determine that a particular event has occurred. The computing device can analyze the measurements to determine the particular event occurred based on the event configuration data. When the particular event occurs, the computing device can perform one or more remedial actions.

Disclosed herein is a vehicle theft prevention device. The device can include a data store including event configuration data. The device can include one or more sensors that can sense various types of measurements proximate to a vehicle. The device can include a computing device in communication with the sensors. The computing device can read measurements from the sensors and determine that a particular event has occurred. The computing device can analyze the measurements to determine the particular event occurred based on the event configuration data. When the particular event occurs, the computing device can perform one or more remedial actions.

An unsupervised learning-based detection method according to one technical aspect of the present disclosure relates to an unsupervised learning-based detection method using a supervised-learned model, and includes: generating a first plurality of matrix data on the basis of driving data; generating encoding information by encoding the first plurality of matrix data using a convolutional neural network; modeling a time series feature of the encoding information by using a long short-term memory (LSTM) network, so as to derive a correlation between variables according to a time series; re-implementing a second plurality of matrix data through a deconvolution calculation of the correlation between the variables according to the time series; and determining whether the driving data corresponds to a pre-supervised learned driver profile, on the basis of a difference between the first plurality of matrix data and the second plurality of matrix data.

An apparatus and a method for controlling a vehicle door are provided to automatically unlock a door locked in vehicle crash. The apparatus includes a main battery, a sub-battery, a door actuator to lock or unlock the vehicle door, and a door controller to unlock the vehicle door which is locked by supplying electric power to the door actuator through the sub-battery, when the door controller determines that supply of electric power from the main battery is failed in the vehicle crash.

An apparatus and a method for controlling a vehicle door are provided to automatically unlock a door locked in vehicle crash. The apparatus includes a main battery, a sub-battery, a door actuator to lock or unlock the vehicle door, and a door controller to unlock the vehicle door which is locked by supplying electric power to the door actuator through the sub-battery, when the door controller determines that supply of electric power from the main battery is failed in the vehicle crash.

A method for detecting theft of a vehicle, includes the following steps: detecting a vehicle movement by an inductive vehicle charging system, checking the authorization for a detected vehicle movement, and outputting a message signal if an unauthorized vehicle movement is detected. The checking of the authorization for a detected vehicle movement is started after the start of a vehicle charging process.

A method for detecting theft of a vehicle, includes the following steps: detecting a vehicle movement by an inductive vehicle charging system, checking the authorization for a detected vehicle movement, and outputting a message signal if an unauthorized vehicle movement is detected. The checking of the authorization for a detected vehicle movement is started after the start of a vehicle charging process.

An approach is provided for detecting a vehicle door opening or closing event based on sensor data collected during a vehicle idle/stop state. The approach, for example, involves collecting sensor data from one or more sensors of a vehicle, a mobile device in the vehicle, or a combination thereof. The approach also involves determining a time period when the vehicle is in an idle state based on the sensor data. The approach further involves determining a vehicle door open event or a vehicle door close event based on pressure sensor data from at least one pressure sensor of the mobile device collected during the time period. The approach further involves providing the vehicle door open event or the vehicle door close event as an output.

An approach is provided for detecting a vehicle door opening or closing event based on sensor data collected during a vehicle idle/stop state. The approach, for example, involves collecting sensor data from one or more sensors of a vehicle, a mobile device in the vehicle, or a combination thereof. The approach also involves determining a time period when the vehicle is in an idle state based on the sensor data. The approach further involves determining a vehicle door open event or a vehicle door close event based on pressure sensor data from at least one pressure sensor of the mobile device collected during the time period. The approach further involves providing the vehicle door open event or the vehicle door close event as an output.

Systems and methods for limiting functionality of a vehicle are described. In one example, vehicle feature modules may specify vehicle behaviors and vehicle operation is limited according to the specified behaviors. Vehicle actuators may be adjusted to limit vehicle operation according to the specified vehicle behaviors. The vehicle behaviors may apply to powertrain systems, navigation systems, climate control systems, lighting systems and other vehicle systems.