Gm Global Technolgoy Operations, Inc.

Gm Cruise Holdings LLC

Pilot Ai Labs, Inc.

Brian Pierce

Drew Gross

Gillian Langor

Jonathan Su

Haven Lau

Ankit Kumar

Elliot English

Yun Jiang

Anton Mario Bongio Karrman

Shaogang Wang

The present disclosure is directed to combining the strengths of different methods of analyzing collected sensor data to updating a driving pattern of an automated vehicle (AV). This may include combining data from sets of data that track movement of objects over time with instantaneously received sensor data based on a series of steps that include accessing data that tracks the motion of objects in the field of view of a sensing apparatus, receiving current sensor data that includes a component of current or instantaneous object motion, and identifying whether controls of AV should be maintained or changed. When controls of the AV are maintained, an AV may be controlled to stay in driving in a same lane of a roadway at a same velocity. When controls of an AV are changed, changes may include applying, increasing a velocity, or altering the course of the AV.

Unified radar perception architecture

Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Micro-authorization of remote assistance for an autonomous vehicle

In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

System and method for improved general object detection using neural networks

Growing community of inventors

Santa Clara, CA, United States of America

Brian Pierce