Waymo LLC

X Development LLC

Boston Dynamics, Inc.

Google Inc.

Jared Stephen Russell

Jens-Steffen Ralf Gutmann

Tobias Kunz

Julian Mac Neille Mason

Nathaniel Fairfield

Dmitri A Dolgov

Christopher Paul Urmson

Daniel Lynn Larner

Fang Da

Laura Estelle Lindzey

James Joseph Kuffner

Kai Ding

Julian MacNeille Mason

Geoffrey Lalonde

Pei Sun

Guillaume Dupre

Johan Engstrom

Kurt Mauro Dresner

Mark Andrew Paskin

Ethan Rublee

Ioan-Alexandru Sucan

Robert Holmberg

Eric Deng

Tirthkumar Nilaykumar Pandya

Clovis Scotti

Eric Schoenfeld

Brian Cohen

Anne Hobbs Dorsey

Anup Menon

A first driving solution for a vehicle along a portion of a route is determined based on an agent detected in the vehicle's environment following a first trajectory of a plurality of possible trajectories. A switching time is determined for the vehicle to deviate from the first driving solution for a situation in which the agent is following a second trajectory of the plurality of possible trajectories. The first driving solution is revised such that the vehicle will be able to switch from the revised first driving solution to another driving solution at the switching time in case if the detected agent is following the second trajectory.

Risk mitigation in speed planning

The technology relates controlling an autonomous vehicle through a multi-lane turn. In one example, data corresponding to a position of the autonomous vehicle in a lane of the multi-lane turn, a trajectory of the autonomous vehicle, and data corresponding to positions of objects in a vicinity of the autonomous vehicle may be received. A determination of whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane may be made based on a position of the autonomous vehicle in the lane relative to the positions of the objects. The trajectory of the autonomous vehicle through the lane may be adjusted based on whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane. The autonomous vehicle may be controlled based on the adjusted trajectory.

Controlling vehicles through multi-lane turns

An autonomous vehicle is operated along a route according to a nominal driving solution that takes into account one or more first constraints including a first predicted trajectory for an agent vehicle. An alternate scenario is determined based on one or more second external constraints that include a second predicted trajectory for the agent vehicle different from the first predicted trajectory. A risk factor on the nominal driving solution is determined for the alternative scenario, and a secondary driving solution is determined based on the risk factor and the one or more second external constraints. A candidate switching point is identified where the secondary driving solution diverges from the nominal driving solution, and the nominal driving solution is revised up to the candidate switching point based on the secondary driving solution. The autonomous vehicle is then operated based on the revised nominal driving solution.

Tree policy planning for autonomous vehicle driving solutions

The technology relates to planning trajectories for self-driving vehicles in order to transport passengers or cargo from a pickup location to a destination. Trajectory planning includes generating a speed plan for an upcoming portion of the trip in view of one or more constraints. The constraints may be due to proximity to an adjacent vehicle or other road user, and can include projected overlaps between the vehicle and other objects in the vehicle's nearby environment. Certain constraints may be binary or otherwise discontinuous in nature, in which a condition either exists at a given point in time or it does not. Noise in sensor data or prediction models may trigger such binary conditions, which in turn may cause the vehicle to alter the speed plan.

Permeable speed constraints

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

The technology relates to controlling a vehicle in an autonomous driving mode. For example, sensor data identifying a plurality of objects may be received. Pairs of objects of the plurality of objects may be identified. For each identified pair of objects of the plurality of objects, a similarity value which indicates whether the objects of that identified pair of objects can be responded to by the vehicle as a group may be determined. The objects of one of the identified pairs of objects may be clustered together based on the similarity score. The vehicle may be controlled in the autonomous mode by responding to each object in the cluster in a same way.

Semantic object clustering for autonomous vehicle decision making

The technology relates to controlling a vehicle in an autonomous driving mode. In one instance, sensor data identifying an object in an environment of the vehicle may be received. A first path of a first trajectory where the vehicle will pass the object may be determined. A function is used to determining a first maximum speed of the vehicle based on a predetermined minimum lateral clearance between the object and the vehicle. The first maximum speed may be used to determine whether an actual lateral clearance between the object and the vehicle will meet the predetermined minimum lateral clearance. The determination of whether the actual lateral clearance will meet the predetermined minimum lateral clearance may be used to generate a first speed plan for the first trajectory. The vehicle may be controlled in the autonomous driving mode according to the first trajectory including the first speed plan and the first path.

Speed-dependent required lateral clearance for autonomous vehicle path planning

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Assessing surprise for autonomous vehicles

Aspects of the disclosure relate to detecting sidewalks adjacent to roads. In this regard, a set of potential sidewalk areas adjacent to one or more roads in a vehicle's vicinity may be determined based on map data. Topology data for the set of potential sidewalk areas may be generated based on sensor data received from a perception system of the vehicle. The set of potential sidewalks may be filtered to remove areas unlikely to include a sidewalk. The vehicle may be operated based on the filtered set of potential sidewalk areas, which may include taking precautionary measures when within a predetermined distance from any of the filtered set of potential sidewalks.

Sidewalk detection for pedestrian behavior modeling

A simulation may be used to determine a difference between progress of a manually-driven vehicle and progress of a simulated autonomous vehicle. The method includes retrieving log data collected for the manually-driven vehicle driving along a route, generating a plurality of path segments for a portion of the route. The plurality of path segments corresponds to points in a lane that the manually-driven vehicle traveled through on the portion of the route. The method also includes running, using a software of the autonomous vehicle, a simulation of the autonomous vehicle driving along the plurality of path segments, extracting metrics from the log data and the simulation, and determining the difference between a first progress of the manually-driven vehicle and a second progress of the simulated autonomous vehicle based on the metrics.

Using simulations to identify differences between behaviors of manually-driven and autonomous vehicles

The technology relates to controlling a vehicle in an autonomous driving mode. For instance, sensor data identifying an object in an environment of the vehicle may be received. A grid including a plurality of cells may be projected around the object. For each given one of the plurality of cells, a likelihood that the object will enter the given one within a period of time into the future is predicted. A contour is generated based on the predicted likelihoods. The vehicle is then controlled in the autonomous driving mode in order to avoid an area within the contour.

Pedestrian behavior predictions for autonomous vehicles

Aspects of the disclosure relate to generating a speed plan for an autonomous vehicle. As an example, the vehicle is maneuvered in an autonomous driving mode along a route using pre-stored map information. This information identifies a plurality of keep clear regions where the vehicle should not stop but can drive through in the autonomous driving mode. Each keep clear region of the plurality of keep clear regions is associated with a priority value. A subset of the plurality of keep clear regions is identified based on the route. A speed plan for stopping the vehicle is generated based on the priority values associated with the keep clear regions of the subset. The speed plan identifies a location for stopping the vehicle. The speed plan is used to stop the vehicle in the location.

Planning stopping locations for autonomous vehicles

The disclosure provides a method for operating an autonomous vehicle. To operate the autonomous vehicle, a plurality of lane segments that are in an environment of the autonomous vehicle is determined and a first object and a second object in the environment are detected. A first position for the first object is determined in relation to the plurality of lane segments, and particular lane segments that are occluded by the first object are determined using the first position. According to the occluded lane segments, a reaction time is determined for the second object and a driving instruction for the autonomous vehicle is determined according to the reaction time. The autonomous vehicle is then operated based on the driving instruction.

Operating an autonomous vehicle according to road user reaction modeling with occlusions

The disclosure provides for a method for determining a route for passenger comfort and operating a vehicle according to the determined route. To start, a set of routes from a start location to an end location may be determined. Each route includes one or more portions. For each route of the set of routes, a total motion sickness value is determined based on a sway motion sickness value, a surge motion sickness value, and a heave motion sickness value for each of the given portions. The total motion sickness value for a route reflects a likelihood that a user will experience motion sickness while in a vehicle along the route. A route may then be selected from the set of routes based on the total motion sickness value of each route of the set of routes, and the vehicle may be maneuvered according to the selected route.

Growing community of inventors

San Francisco, CA, United States of America

Jared Stephen Russell