Waymo LLC

David Yonchar Margines

Peter Michal Pawlowski

Michael Steven Montemerlo

Carlos Hernandez Esteban

David Harrison Silver

Colin Andrew Braley

Dirk Haehnel

Laurens Andreas Feenstra

Saksiri Tanphaichitr

Tacettin Dogacan Guney

Jeffrey Min

Joshua Seth Herbach

Edward Hsiao

Katharine Patterson

Maoqing Yao

Yosuke Higashi

Michael Buchanan

Austin Daniel Abrams

Aleksandar Gabrovski

Ingrid Fiedler

Carlos Rivera

Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

Generating scouting objectives

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, a current state of a traffic light may be determined. One of a plurality of yellow light durations may be selected based on the current state of the traffic light. When the traffic light will turn red may be predicted based on the selected one. The prediction may be used to control the vehicle in the autonomous driving mode.

Yellow light durations for autonomous vehicles

Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Service area maps for autonomous vehicles

Example embodiments relate to pedestrian countdown signal classification to increase pedestrian behavior legibility. An example embodiment includes a method that includes obtaining, by a computing system of a vehicle, a camera image patch. The method further includes determining, by the computing system, using the camera image patch and a pedestrian countdown signal classifier model, a state of a pedestrian countdown signal. The method also includes determining, by the computing system based on the state of the pedestrian countdown signal, a prediction of whether a pedestrian will enter a crosswalk governed by the pedestrian countdown signal. And the method includes, based on the prediction, causing, by the computing system, the vehicle to perform an invitation action that invites a pedestrian to enter the crosswalk.

Pedestrian countdown signal classification to increase pedestrian behavior legibility

Methods, systems, and apparatus for generation and use of surfel maps to plan for occlusions. One of the methods includes receiving a previously-generated surfel map depicting an area in which a vehicle is located, the surfel map comprising a plurality of surfels, each surfel corresponding to a respective different location in the area in which a vehicle is located; receiving, from one or more sensors, sensor data representing the area in which the vehicle is located; determining, based on the sensor data, that the area in which a vehicle is located includes a dynamic object having a changed shape relative to its representation in the surfel map; and generating an updated path for the vehicle to travel that avoids an occlusion by the changed shape of the dynamic object of a line of sight of one or more sensors to an area of interest.

Planning for occlusions with surfel maps

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using traffic light viewsheds. One of the methods includes obtaining, by an on-board computer system of vehicle, viewshed data comprising a plurality of viewsheds for a plurality of respective traffic lights. An indication of a candidate driving plan that uses input from a traffic light detection of a particular traffic light having a viewshed is received. If a current location of the vehicle is within the particular viewshed for the particular traffic light, execution of the candidate driving plan is conditioned based on whether the current location of the vehicle is within the particular viewshed for the particular traffic light.

Traffic light viewsheds

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Long range localization with surfel maps

Aspects of the disclosure provide for the generation of a service area map for autonomous vehicles. For instance, graph nodes of a road network may be iterated through in order to identify a set of reachable graph nodes based on a set of routing parameters that define driving limits for the autonomous vehicles. The road network may include the graph nodes as well as edges connecting ones of the graph nodes. A set of S2 cells may be identified based on the set of reachable graph nodes. Vertices of each S2 cell of the set of S2 cells may be determined based on whether each S2 cell of the set of S2 cells is occupied by any of the graph nodes of the set of reachable graph nodes. Contours through cells may be drawn based on the scores. The service area map may be generated using the contours.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing data characterizing a first region in an environment to generate a prediction characterizing a second region in the environment. One of the methods includes obtaining surfel data comprising a plurality of surfels; obtaining sensor data for a plurality of locations in a first region of the environment; determining, from the surfel data, a plurality of first surfels corresponding to respective locations in the first region of the environment; determining, using the first surfels and the sensor data, a difference between i) a first representation of the first region of the environment corresponding to the first surfels and ii) a second representation of the first region of the environment corresponding to the sensor data; and generating a respective object prediction for one or more locations in a second region of the environment.

Detecting environment changes using surfel data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate a prediction for a state of an environment. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises an uncertainty measure; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more particular surfels corresponding to respective locations of the obtained sensor data; and combining the surfel data and the sensor data to generate a respective object prediction for each of the one or more locations of the obtained sensor data.

Growing community of inventors

Sunnyvale, CA, United States of America

David Yonchar Margines