Aurora Operations, Inc.

Stephen O'Hara

Benjamin Kaplan

Niels Joubert

Ariel Quinn

Chris Leibs

Kevin Ullmann

An autonomous vehicle control system and method may utilize a dual mode map including sparse map data for some portions of an environment that lacks some of the data maintained in dense map data for other portions of the environment. Sparse map data may be used, for instance, to address a recently-established construction area on a roadway that is incompatible with dense map data that was previously used to operate on the roadway, enabling operation of an autonomous vehicle in the construction area to proceed even in the absence of dense map data for the construction area, e.g., by dynamically augmenting the sparse map data to incorporate additional data sensed by a perception system of the autonomous vehicle.

Dual mode map for autonomous vehicle

A method is provided, that includes: receiving camera data from a perception system of an autonomous vehicle; and providing the camera data to a visual language model, where the visual language model includes a mapping of a corpus of images and a corpus of text to a common parameter space. The method further includes: receiving from the visual language model an output corresponding to one or more text tokens; accessing a configuration file comprising a plurality of text tokens representing a plurality of objects or events of interest to the autonomous vehicle; and identifying a respective object or event of interest in an environment of the autonomous vehicle by determining that a text token of the output matches a respective one of the plurality of text tokens in the configuration file. The autonomous vehicle can then be controlled based at least in part on the respective object or event of interest.

Visual language models for perception

A live map system may be used to propagate observations collected by autonomous vehicles operating in an environment to other autonomous vehicles and thereby supplement a digital map used in the control of the autonomous vehicles. In addition, a live map system in some instances may be used to propagate location-based teleassist triggers to autonomous vehicles operating within an environment. A location-based teleassist trigger may be generated, for example, in association with a teleassist session conducted between an autonomous vehicle and a remote teleassist system proximate a particular location, and may be used to automatically trigger a teleassist session for another autonomous vehicle proximate that location and/or to propagate a suggested action to that other autonomous vehicle.

Remote live map system for autonomous vehicles

Discrepancies between mapped lanes on a roadway and perceived lanes perceived by the perception system of an autonomous vehicle may be identified and used to augment map data used by the autonomous vehicle, thereby enabling the autonomous vehicle to operate even in the event of the identified discrepancy.

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Fort Collins, CO, United States of America

Stephen O'Hara