Gm Global Technolgoy Operations, Inc.

Benjamin Earle Weinstein-Raun

Eric Lujan

Elliot Branson

Drew Gross

Gabriel Warshauer-Baker

Nathan Mandi

An autonomous vehicle is described herein. The autonomous vehicle comprises a first sensor and a second sensor having limited fields of view, an articulation system, and a computing system. The computing system determines a first region and a second region external to the autonomous vehicle based on a sensor prioritization scheme comprising a ranking of regions surrounding the autonomous vehicle. The computing system then causes the articulation system to orient the first sensor towards the first region and the second region towards the second region. Responsive to receiving a sensor signal from the first sensor indicating that an object has entered a field of view of the first sensor, the computing system determines a third region having a higher ranking than the second region within the sensor prioritization scheme. The computing system then causes the articulation system to orient the second sensor towards the third region.

Controlling articulating sensors of an autonomous vehicle

An autonomous vehicle is described herein. The autonomous vehicle comprises sensor systems that generate sensor signals and a computing system. The computing system generates an awareness graph based upon the sensor signals. The awareness graph comprises nodes connected by directed edges. The nodes include a first node assigned to a first maneuver that is to be executed by a vehicle in a vicinity of the autonomous vehicle and a second node assigned to a second maneuver that is to be executed by the autonomous vehicle. The directed edges include a directed edge that extends from the second node to the first node indicating that execution of the second maneuver is dependent upon execution of the first maneuver. The autonomous vehicle determines an ordering of the first maneuver and the second maneuver based on the awareness graph and executes the second maneuver in accordance with the ordering.

Maneuver-based interaction system for an autonomous vehicle

Systems and method are provided for controlling a vehicle. In one embodiment, a vehicle includes: one or more onboard sensing devices, one or more onboard actuators, and a controller that, by a processor, obtains sensor data from the one or more sensing devices, identifies an assistance event based at least in part on the sensor data, determines an automated response to the assistance event based at least in part on the sensor data and an autonomous assistance policy, and autonomously operates the one or more actuators onboard the vehicle in accordance with the automated response. In exemplary embodiments, the autonomous assistance policy is determined using inverse reinforcement learning to optimize a reward function and mimic a remote human assistor.

Autonomous vehicle operations with automated assistance

Systems and method are provided for controlling a vehicle. In various embodiments, a method of path planning for a vehicle includes receiving sensor data relating to an environment associated with the vehicle; defining a region of interest for the vehicle, based on the sensor data; defining a graph comprising a plurality of nodes, each of the plurality of nodes comprising a state of the vehicle and an associated cost, based on a cost function as applied to the state of the vehicle, at one of a plurality of points in time; and performing, via a processor, a search of the graph, based on the associated costs of each node of the graph, to determine a selected path for the vehicle through the region of interest that minimizes a total cost via the graph.

Growing community of inventors

Berkeley, CA, United States of America

Benjamin Earle Weinstein-Raun