Zoox, Inc.

Christopher James Gibson

Taylor Andrew Arnicar

Byungil Jeong

Kai Zhenyu Wang

Christopher Patton

Zachary Davis

Azeem Zaheer Ghumman

Chelsea Lauran Williams

Techniques are discussed herein for detecting and resolving disparities between sensor data perceived by sensor-based systems operating in an environment and corresponding map data of the environment. Sensor data may be captured by a vehicle or other sensor system operating in an environment, including representations of objects at various locations in the environment. The object representations may be used to determine disparities between the sensor data and map data associated with the same locations. Such disparities may be caused by, for example, physical changes in the environment, map data changes, and/or localization errors of the sensor system. The techniques discussed herein further include analyzing the map data and sensor data to determine causes associated with the disparities, and resolving the disparities by updating the map data and/or transmitting updated sensor configuration data to sensor systems in the environment.

Detecting and resolving disparities between map data and environments perceived by sensor systems

Techniques for validating operation of vehicle systems using map data are described herein. The techniques may include receiving sensor data associated with an environment in which a vehicle is operating and generating estimated map data based at least in part on the sensor data. Additionally, stored map data may be received and compared with the estimated map data. If it is determined that an inconsistency exists between the estimated map data and the stored map data, the techniques may include determining whether the inconsistency is attributable to an error associated with the stored map data, the sensor data, or a localization system of the vehicle. As such, one or more specific remedial actions may then be performed based at least in part on determining that the error is associated with at least one of the stored map data, the sensor data, or the localization system.

Monitoring map data for validation of vehicle systems

In examples, a method comprises receiving input from a first user relating to a first variable of a first portion of a road network, determining first semantic data associated with the first variable, determining the absence of a lock for the first portion based on the first semantic data, and causing a first lock to be implemented for the first portion based on the first semantic data. The first lock in examples indicates that users other than the first user are to be prevented from making changes to a first set of variables of the first portion that are associated with the first semantic data.

Collaborative road network access

The present disclosure is related to generating map data explicitly indicating a total drivable surface, which may include multiple types of drivable surfaces. For instance, a given portion of a map may include map data indicating a combination of various drivable surfaces, such as road segments, lane properties, intersections, parking areas, shoulders, driveways, etc. Examples of the present disclosure join these different types of drivable surfaces into combined map data that explicitly indicates a total drivable surface, such as a perimeter boundary indicating or representing a transition from a drivable surface to a non-drivable surface. The map data indicating the total drivable surface may be searched to determine information related to a drivable surface boundary, such as location and type. This boundary information may be used in various contexts, such as when planning a trajectory or remotely controlling a vehicle.

Drivable surface map for autonomous vehicle navigation

Techniques related to generating map data explicitly indicating a total drivable surface, which may include multiple types of drivable surfaces, are described herein. For instance, a given portion of a map may include map data indicating a combination of various drivable surfaces, such as road segments, lane properties, intersections, parking areas, shoulders, driveways, etc. Examples include joining these different types of drivable surfaces into combined map data that explicitly indicates a total drivable surface, such as a perimeter boundary indicating or representing a transition from a drivable surface to a non-drivable surface. The map data indicating the total drivable surface may be searched to determine information related to a drivable surface boundary, such as location and type. This boundary information may be used in various contexts, such as when planning a trajectory or remotely controlling a vehicle.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

Capturing features for determining routes

Techniques are disclosed for updating map data. The techniques may include detecting a traffic light in a first image, determining, based at least in part on the traffic light detected in the first image, a proposed three-dimensional position of the traffic light in a three-dimensional coordinate system associated with map data. The proposed three-dimensional position may then be projected into a second image to determine a two-dimensional position of the traffic light in the second image and the second image may be annotated, as an annotated image, with a proposed traffic light location indicator associated with the traffic light. The techniques further include causing a display to display the annotated image to a user, receiving user input associated with the annotated images, and updating, as updated map data, the map data to include a position of the traffic light in the map data based at least in part on the user input.

Growing community of inventors

Belmont, CA, United States of America

Christopher James Gibson