Gm Global Technolgoy Operations, Inc.

Siddhartho Bhattacharya

Peter Ming Gao

Wei Mou

Eric Lujan

Elliot Branson

Jeremy Allan

Gabriel Warshauer-Baker

Mark Liu

Yiqi Gao

Haggai Megged Nuchi

Jonathan Uesato

An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Autonomous vehicle that is controlled based upon output of a Bayesian object classifier system

Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via one or more sensing devices, sensor data relating to an environment associated with a vehicle, wherein the vehicle is substantially stationary and has an intended path corresponding to entering a traffic flow of a first lane. The method includes determining the presence of an obstacle that at least partially occludes a view of the first lane by a first sensing device of the one or more sensing devices, and positioning, with a processor, the vehicle with respect to the obstacle to improve the view of the first lane by the first sensing device without significantly obstructing the first lane. The first lane is then monitored via the first sensing device to determine when the intended path clear.

Systems and methods for entering traffic flow in autonomous vehicles

Systems and method are provided for controlling an autonomous vehicle. A camera configured to capture an image, and a controller can execute an autonomous driving system (ADS) that classify that image. The ADS comprises a classification system for classifying objects in an environment within a driveable area of the autonomous vehicle. The classification system comprises a processor configured to execute a region proposal generator module and an image classification module. The region proposal generator module generates a set of bounding box region proposals for the image. The bounding box region proposals are selected areas of the image that include objects to be classified. The image classification module classifies, via a neural network executed by the processor, the objects from the image that are within one of the bounding box region proposals.

Systems and methods for visual classification with region proposals

Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via sensing devices, sensor data relating to an environment associated with a vehicle, and determining, with the sensor data, the presence of an obstacle that at least partially obstructs an intended path of the vehicle in a first lane adjacent to a second lane and at least partially occludes a view of the second lane by a first sensing device. The method further includes positioning the vehicle with respect to the obstacle to improve the view of the second lane by the first sensing device without significantly obstructing the second lane, determining a second path that circumnavigates the obstacle and rejoins the intended path, and monitoring the opposing lane via the first sensing device to determine when the second path is clear.

Systems and methods for maneuvering around obstacles in autonomous vehicles

Growing community of inventors

San Jose, CA, United States of America

Siddhartho Bhattacharya