A method and system for transmitting an access instruction for accessing a vehicle is provided. The system includes a sensory operable to be installed in association with a seat of the vehicle, and a processor that is configured to receive, from a device that is external to the vehicle, a request for accessing the vehicle, determine, using the sensor, an occupation status of the seat of the vehicle, and transmit an access instruction to the device, wherein the access instruction is based on the occupation status of the seat.

Described is a system and method for the classification of agents based on agent movement patterns. In operation, the system receives position data of a moving agent from a camera or sensor. Motion data of the moving agent is then extracted and used to generate a predicted future motion of the moving agent using a set of pre-calculated Echo State Networks (ESN). Each ESN represents an agent classification and generates a predicted future motion. A prediction error is generated for each ESN by comparing the predicted future motion for each ESN with actual motion data. Finally, the agent is classified based on the ESN having the smallest prediction error.

Described is a system and method for the classification of agents based on agent movement patterns. In operation, the system receives position data of a moving agent from a camera or sensor. Motion data of the moving agent is then extracted and used to generate a predicted future motion of the moving agent using a set of pre-calculated Echo State Networks (ESN). Each ESN represents an agent classification and generates a predicted future motion. A prediction error is generated for each ESN by comparing the predicted future motion for each ESN with actual motion data. Finally, the agent is classified based on the ESN having the smallest prediction error.

A vehicle control device executes driving assistance control for assisting driving a host vehicle by a driver. The vehicle control device stops the driving assistance control under execution when a predetermined actuator of the host vehicle is operated. The vehicle control device executes, as the driving assistance control, abnormality countermeasure control for securing safe traveling of the host vehicle when the driver falls into an abnormal state having a problem in driving the host vehicle, and when the predetermined actuator is operated, and when the abnormality countermeasure control is not being executed, stops the driving assistance control, and when the abnormality countermeasure control is being executed, does not stop the driving assistance control including the abnormality countermeasure control.

A vehicle control device executes driving assistance control for assisting driving a host vehicle by a driver. The vehicle control device stops the driving assistance control under execution when a predetermined actuator of the host vehicle is operated. The vehicle control device executes, as the driving assistance control, abnormality countermeasure control for securing safe traveling of the host vehicle when the driver falls into an abnormal state having a problem in driving the host vehicle, and when the predetermined actuator is operated, and when the abnormality countermeasure control is not being executed, stops the driving assistance control, and when the abnormality countermeasure control is being executed, does not stop the driving assistance control including the abnormality countermeasure control.

The present disclosure relates to an apparatus and method for controlling a brake system based on a driver's forward gaze.

The present disclosure relates to an apparatus and method for controlling a brake system based on a driver's forward gaze.

A system for characterizing driver behavior includes an interface and a processor. The interface is configured to receive a set of vehicle data, wherein the set of vehicle data comprises embedded image vectors that characterize vehicle data over a short time scale. The processor is configured to determine, using a long time scale model, a long time scale label based at least in part on the embedded image vectors.

A computer vision processor of a camera generates hyperzooms for persons or vehicles from image frames captured by the camera. The hyperzooms include a first hyperzoom associated with the persons or vehicles. The computer vision processor tracks traffic patterns of the persons or vehicles while obviating network usage by the camera by predicting positions of the persons or vehicles using a Kalman Filter from the first hyperzoom. The persons or vehicles are detected in the second hyperzoom. The positions of the persons or vehicles are updated based on detecting the persons or vehicles in the second hyperzoom. The first hyperzoom is removed from the camera. Tracks of the persons or vehicles are generated based on the updated positions. The second hyperzoom is removed from the camera. Track metadata is generated from the tracks for storing in a key-value database located on a non-transitory computer-readable storage medium of the camera.

A computer vision processor of a camera generates hyperzooms for persons or vehicles from image frames captured by the camera. The hyperzooms include a first hyperzoom associated with the persons or vehicles. The computer vision processor tracks traffic patterns of the persons or vehicles while obviating network usage by the camera by predicting positions of the persons or vehicles using a Kalman Filter from the first hyperzoom. The persons or vehicles are detected in the second hyperzoom. The positions of the persons or vehicles are updated based on detecting the persons or vehicles in the second hyperzoom. The first hyperzoom is removed from the camera. Tracks of the persons or vehicles are generated based on the updated positions. The second hyperzoom is removed from the camera. Track metadata is generated from the tracks for storing in a key-value database located on a non-transitory computer-readable storage medium of the camera.