Waymo LLC

Google Inc.

Ioan-Alexandru Sucan

Nathaniel Fairfield

Dong Li

David Harrison Silver

Joshua Seth Herbach

Stéphane Ross

David Ian Franklin Ferguson

Shir Judith Yehoshua

Collin Winter

Aida Khosroshahi

Michael R James

Christopher Kennedy Ludwick

Matthew Paul McNaughton

Andrew Barton-Sweeney

Ryan Joseph Andrade

Emily Chi

Misha Balakhno

Victor Puchkarev

David Ian Ferguson

Jared Stephen Russell

Jens-Steffen Ralf Gutmann

Michael Epstein

Jennifer Iglesias

Vishay Nihalani

Aishwarya Parasuram

Poonam Suryanarayan

Fang Da

Omer Baror

Matthew Paul Mcnaughton

Jian Leong

Yutaka Leon Suematsu

Chien-Yu Ko

Steven Vozar

Jonathan Daudelin

Tian Jin

Ryan Luna

Jory Denny

Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

Exception handling for autonomous vehicles

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode or an autonomous vehicle. For instance, a polygon representative of the shape and location of a first object may be received. A polyline contour representation of a portion of a polygon representative of the shape and location of a second object may be received. The polyline contour representation may be in half-plane coordinates and including a plurality of vertices and line segments. Coordinates of the polygon representative of the first object may be converted to the half-plane coordinate system. A collision location between the polyline contour representation and the polygon representative of the first object may be determined using the converted coordinates. The autonomous vehicle may be controlled in the autonomous driving mode to avoid a collision based on the collision location.

Polyline contour representations for autonomous vehicles

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

Systems for implementing fallback behaviors for autonomous vehicles

Example embodiments relate to using remote assistance to maneuver an autonomous vehicle to a location. A computing device used by a remote operator may receive a request for assistance from a vehicle that indicates the vehicle is stopped at a first location with one or more navigation options for enabling the vehicle to navigate from the first location to a second location. At least one navigation option includes a maneuver technique that requires operator approval prior to execution. The computing device may then display a graphical user interface (GUI) that conveys the one or more navigation options. Based on detecting a selection of a particular navigation option, the computing device may transmit instructions to the vehicle to perform the particular navigation option. The vehicle may configured to navigate from the first location to the second location by performing the particular navigation option while monitoring for changes in the environment.

Methods and systems for using remote assistance to maneuver an autonomous vehicle to a location

Aspects of the disclosure provide a method of generating and following planned trajectories for an autonomous vehicle. For instance, a baseline for a planned trajectory that the autonomous vehicle can use to follow a route to a destination may be determined. A stopping point corresponding to a traffic control that will cause the autonomous vehicle to come to a stop using the baseline may be determined. Sensor data identifying objects and their locations may be received. A plurality of constraints may be generated based on the sensor data. A planned trajectory may be generated using the baseline, the stopping point, and the plurality of constraints, wherein constraints beyond the stopping point are ignored.

Trajectory limiting for autonomous vehicles

Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

Predicting trajectories of objects based on contextual information

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode using trajectories. For instance, a trajectory may be received by one or more first computing devices from one or more second computing devices. While the first computing devices are controlling the vehicle in the autonomous driving mode based on the trajectory, an error may be generated by second computing devices. Whether the error is a recoverable error may be determined, and if so, the second computing devices attempt to generate a new trajectory. When the second computing devices generate the new trajectory, the vehicle may be controlled by the first computing devices according to the new trajectory.

Planner system recovery for autonomous vehicles

Example embodiments relate to gradually adjusting a vehicle sensor perspective using remote assistance. A computing device may receive a request for assistance from a vehicle operating in an environment. The request indicates that the vehicle is stopped at a location with a sensor perspective of the environment that is at least partially occluded. Responsive to receiving the request for assistance, the computing device may display a graphical user interface (GUI) that represents a current state of the vehicle and includes a selectable option configured to enable the vehicle to gradually move forward a predefined distance. The computing device may detect a selection of the selectable option and transmit instructions that enable the vehicle to gradually move forward the predefined distance. The vehicle can then gradually move forward the predefined distance while also monitoring for one or more changes in the environment responsive to receiving the instructions.

Methods and systems for gradually adjusting vehicle sensor perspective using remote assistance

The disclosure relates to training models for identification of events likely to cause discomfort to passengers of autonomous vehicles and for assessment of overall ride quality of autonomous vehicle rides. For instance, ride data may be associated with a ride quality value indicative of a level of discomfort and/or a first overall ride quality value indicating an overall ride quality provided by the passenger for the first ride. This ride data may be used to train a model such that the model is configured to, in response to receiving ride data for a second ride as input, output a list of events likely to cause discomfort to a passenger during the second ride and/or such that the model is configured to, in response to receiving second ride data for a second ride as input, output a second overall ride quality value for the second ride.

Assessing ride quality for autonomous vehicles

Growing community of inventors

Mountain View, CA, United States of America

Ioan-Alexandru Sucan