Gm Global Technolgoy Operations, Inc.

Siddhartha Gupta

Wei Tong

Jacob Alan Bond

Sean T Coughlin

Maria Del Mar Davila

Oded Bialer

Upali Priyantha Mudalige

Zhuoning Yuan

A target tracking system for use in a vehicle interior of a motor vehicle includes a target sensor and an electronic control unit (ECU). The target sensor, e.g., a red, green, blue (RGB) camera, RGB-infrared, or RGB-depth (RGBD) camera, is situated in the vehicle interior and outputs a sensor data stream of a seat surface. The ECU includes a processor and a non-transitory computer-readable storage medium on which is recorded instructions, execution of which by the processor causes the ECU to receive the sensor data stream of the seat surface and segment the sensor data stream into an output file. The ECU also detects a target on the seat surface using the output file, such as a child restraint system, and classifies the target into a target class. The ECU then executes a control action in response to the target class.

Vehicle cabin-based target detection and tracking

An example vehicle interior camera system includes memory configured to store at least one Gaussian process image segmentation model, a vehicle interior camera, and a vehicle control module configured to obtain an image of the interior of the vehicle, extract one or more image crops of a region surrounding a target object in the image, detect and segment a passenger mask in the image, supply the image to the at least one Gaussian process image segmentation model to obtain a region mask associated with the region surrounding the target object, remove the passenger mask from the region mask to extract a target object mask, identify a target object condition according to the target object mask, and control at least one vehicle system or feature in response to identification of the target object condition.

Vehicle interior camera systems

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive intermediate concept constraints at a neural network and train the neural network with training data, training labels, and the at least one of the data constraint, the feature constraint, or the intermediate concept constraint.

Robust neural network learning system

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to determine a pairwise region of interest feature similarity based on features extracted from a first cropped image portion and corresponding point cloud data and features extracted from a second cropped image portion and corresponding point cloud data. The processor is also programmed to determine a loss using a loss function based on the pairwise region of interest feature similarity, wherein the loss function corresponds to at least one a first deep neural network or a second deep neural network. The processor is also programmed to update at least one weight of the at least one of the first deep neural network or the second deep neural network based on the loss.

Computationally efficient unsupervised DNN pretraining

A system of controlling operation of a vehicle includes one or more sensors operatively connected to the vehicle. The sensors are configured to obtain respective data of a scene and include a radar unit. A command unit is adapted to receive the respective data and includes a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is configured to determine an orientation angle of a pedestrian in the scene, a Doppler frequency of the pedestrian and a distance of the pedestrian from a border of a road, based in part on the respective data. The orientation angle is based on a heading of the pedestrian relative to a direction of a road. The command unit is configured to designate a status of the pedestrian as either crossing or not crossing based on the distance, the orientation angle and the Doppler frequency of the pedestrian.

Prediction of pedestrian road crossing with vehicle sensor

An automated driving system includes an object detection system. A neural network image encoder generates image embeddings associated with an image including an object. A neural network text encoder generates concept embeddings associated with each of a plurality of concepts. Each of the plurality of concepts is associated with one of at least two object classes. A confidence score module generates a confidence score for each of the plurality of concepts based on the image embeddings and the concept embeddings associated with the concept. An object class prediction module generates a predicted object class of the object based on an association between a set of concepts of the plurality of concepts having at least two of the highest values of the generated confidence scores and the one of the at least two object classes associated with a majority of the set of concepts.

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Rochester Hills, MI, United States of America

Siddhartha Gupta