Magna Electronics Inc.

Dus Operating Inc.

Heinz Bodo Seifert

Sylvie Wacquant

Hasib Hassan

Iyad Faisal Ghazi Mansour

A vehicular driver monitoring system includes a driver status information acquisition system and an in-vehicle control system of the vehicle. The driver status information acquisition system receives data from a plurality of sensors in the vehicle and determines the driver status responsive to processing of the received data. While the driver is not driving the vehicle and the in-vehicle control system is autonomously driving the vehicle, and responsive to determination that the driver of the vehicle should take over driving the vehicle from the in-vehicle control system, the in-vehicle control system alerts the driver to indicate that the driver should take over driving the vehicle. Responsive to the status of the driver while the driver is not driving the vehicle being indicative of the driver not being able to take over driving the vehicle, the in-vehicle control system continues driving the vehicle and initiates an emergency action.

Vehicular driver monitoring system

An emergency vehicle detection system for a vehicle includes a plurality of camera assemblies having a camera and at least one microphone. At least one processor processes image data captured by the cameras, and processes outputs of the microphones. The at least one processor processes the outputs of the microphones to detect a sound source indicative of a siren emanating from an emergency vehicle exterior of the equipped vehicle, and to determine the direction of the detected sound source. Responsive to detection of the sound source and determination of the direction of the detected sound source, the at least one processor processes image data captured by at least one of the cameras that has its field of view encompassing the detected sound source to confirm that the detected sound source is a siren emanating from an emergency vehicle.

Vehicle acoustic-based emergency vehicle detection

A method of training a heterogeneous convolutional neural network (HCNN) system includes identifying batch sizes for a first task and a second task, defining images for a first batch, a second batch, and a batch x for the first task, defining images for a first batch, a second batch, and a batch y for the second task, training the HCNN using the first batch for the first task, training the HCNN using the first batch for the second task, training the HCNN using the second batch for the first task, training the HCNN using the second batch for the second task. The sequential training continues for each of the batches and each of the tasks until the end of an epoch. When the epoch is complete, the images for each batch and each task are reshuffled.

Sequential training method for heterogeneous convolutional neural network

A heterogeneous convolutional neural network (HCNN) system includes a visual reception system generating an input image. A feature extraction layer (FEL) portion of convolutional neural networks includes multiple convolution, pooling and activation layers stacked together. The FEL includes multiple stacked layers, a first set of layers learning to represent data in a simple form including horizontal and vertical lines and blobs of colors. Following layers capture more complex shapes such as circles, rectangles, and triangles. Subsequent layers pick up complex feature combinations to form a representation including wheels, faces and grids. The FEL portion outputs data to each of: a first sub-network which performs a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table; and a second sub-network which performs a second task of defining a pixel level segmentation to create a segmentation data set.

Heterogeneous convolutional neural network for multi-problem solving

A communication system for multiple vehicles includes a non-vehicle-based communication device operable to receive communications from vehicle-based communication devices of multiple vehicles and to transmit communications to the vehicle-based communication devices of multiple vehicles. Responsive to an occupant of a vehicle of the multiple vehicles rating or classifying an area at which the vehicle is traveling, the vehicle-based communication device of that vehicle communicates the rating or classification to the non-vehicle-based communication device. The vehicle-based communication device of that vehicle also communicates geographical location information pertaining to the area that is rated or classified. The non-vehicle-based communication device communicates the rating or classification for that area to other vehicle-based communication devices of other vehicles.

Vehicle communication system with area classifier

A driver monitoring system for a vehicle includes a vehicle data acquisition system operable to determine vehicle status information after a vehicle is involved in a collision including information pertaining to (i) the collision of the vehicle, (ii) an overturning of the vehicle, (iii) water intrusion into the vehicle and (iv) airbag deployment. A driver status information acquisition system is operable to determine health parameters of the driver of the vehicle. A communication system of the vehicle is operable to wirelessly communicate with a remote assistance system. Responsive to the vehicle status information determined by the vehicle data acquisition system and the driver health parameters determined by the driver information acquisition system, the communication system wirelessly communicates information to the remote assistance system and, responsive to the communication received by the remote assistance system, the remote assistance system determines an appropriate response to the vehicle collision.

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Ortonville, MI, United States of America

Heinz Bodo Seifert