Waymo LLC

Google Inc.

Andrew Barton-Sweeney

Daniel Trawick Egnor

Joshua Seth Herbach

Peter Craig Lombrozo

Daniel Lynn Larner

Michael R James

Russell Leigh Smith

Ioan-Alexandru Sucan

Christopher Kennedy Ludwick

Nolan McPeek-Bechtold

Ryan Joseph Andrade

Diomidis Katzourakis

Emily Chi

James Harris

Vadim Butakov

Ruslan Kurdyumov

Misha Balakhno

Carsten Jensen

Spencer Alexander

Nathaniel Fairfield

Joshua P Switkes

Ryan Cash

David Smith

Craig Latimer

Michael James

Rami Y Hindiyeh

Andre Strobel

Mark Stephen Calleija

Peter Greene

Joel Benito Manrique

Example embodiments relate to techniques for adjusting vehicle behavior based on ambient ground relative wind estimations. An onboard computing system may receive wind data from one or multiple wind sensors positioned onboard a vehicle. The wind data can indicate a direction and a speed of wind propagating in the vehicle's environment. The computing system can also receive navigation data that represents a direction and a speed of the vehicle and then estimate an ambient ground relative wind speed based on the navigation data and the wind data. The computing system can adjust the behavior of the vehicle based on the ambient ground relative wind speed. For instance, the computing system may compare the speed of the ambient ground relative wind to a predefined behavior threshold curve and adjust the behavior of the vehicle based on the comparison.

Methods and systems for adjusting vehicle behavior based on ambient ground relative wind speed estimations

A method includes identifying mass distribution data of an autonomous vehicle (AV). The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes determining, based on the mass distribution data, one or more handling maneuver limits for the AV. The method further includes causing the AV to travel a route based on the one or more handling maneuver limits.

Handling maneuver limits for autonomous driving systems

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

A method includes identifying sensor data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the sensor data, mass distribution data of the AV. The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes causing, based on the mass distribution data, performance of a corrective action associated with the AV.

Mass distribution-informed optimization for autonomous driving systems

A method and system are provided for controlling braking a vehicle in an autonomous driving mode. For instance, the vehicle is controlled in the autonomous driving mode according to a first braking control mode using a first model to adjust the position of a vehicle relative to an expected position of a current trajectory of the vehicle. Using a second model how close to a maximum deviation threshold the vehicle would come if a maximum braking strength for the vehicle was applied is predicted. The maximum deviation threshold provides an allowed forward deviation from the current trajectory. Based on the prediction, the vehicle is controlled in the autonomous driving mode according to a second braking control mode by automatically applying the maximum braking strength.

Braking control behaviors for autonomous vehicles

The technology employs a variable motion control envelope that enables an on-board computing system of a self-driving vehicle to estimate future vehicle driving behavior along an upcoming path, in order to maintain a desired amount of control during autonomous driving. Factors including intrinsic vehicle properties, extrinsic environmental influences and road friction information are evaluated. Such factors can be evaluated to derive an available acceleration model, which defines an envelope of maximum longitudinal and lateral accelerations for the vehicle. This model, which may identify dynamically varying acceleration limits that can be affected by road conditions and road configurations, may be used by the on-board control system (e.g., a planner module of the processing system) to control driving operations of the vehicle in an autonomous driving mode.

Architecture for variable motion control envelope

The disclosure relates to controlling braking behaviors a vehicle in an autonomous driving mode in the event of a hydraulic system failure. For instance, the vehicle may be controlled in the autonomous driving mode according to a first braking control behavior based on a first relationship between position of a brake pedal of the vehicle and amount of deceleration for the vehicle. While controlling the vehicle, a failure of a hydraulic system may be determined. Based on the determination, the vehicle may be controlled in the autonomous driving mode according to a second braking control behavior by moving the brake pedal until the vehicle reaches a deceleration target defined by the second braking control behavior.

Braking control behaviors for autonomous vehicles during hydraulic system failure

A method and apparatus are provided for determining the location of a vehicle. According to one aspect the method and apparatus the movements of motor rotors in a vehicle are monitored and used to determine speed, travel distance and/or travel path of a vehicle may be determined. Using various navigation techniques, the distance and travel path may then be used to determine the vehicle's location. Furthermore, movements of the motor rotors may also be used to report the positions of the steering and drive systems for the purpose of informing the vehicle controller as a method of feedback.

Method and apparatus for determining vehicle location based on motor feedback

Aspects of the disclosure provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Fall back trajectory systems for autonomous vehicles

Growing community of inventors

Berkeley, CA, United States of America

Andrew Barton-Sweeney