Waymo LLC

Austin Daniel Abrams

Laurens Andreas Feenstra

Peter Colijn

Katharine Patterson

Guillaume Dupre

Vishay Nihalani

Jialiu Lin

Carl Yang

Nathaniel Fairfield

Joshua Seth Herbach

David Yonchar Margines

Benjamin Pitzer

Christopher Bowen

Andrew Chan

Carlos Rivera

Aspects of the disclosure provide for determining whether a vehicle is in a loop for an autonomous vehicle. For instance, a route from a current location of the vehicle to a destination for a trip may be generated. Locations traversed by the vehicle during the trip may be tracked. Locations of the route may be compared to the tracked locations to determine an overlap value. Whether the vehicle is in a loop may be determined based on the overlap value.

Detecting loops for autonomous vehicles

A method is provided for managing and tracking scouting tasks to obtain map information using a fleet of autonomous vehicles. For instance, the method includes defining a scouting quest to obtain the map information. The scouting quest includes a plurality of objectives. Each objective is associated with a geographic location from which sensor data is to be captured. The method also includes receiving a first update message from an autonomous vehicle of the fleet. The update message identifies a location of the autonomous vehicle. The method also includes assigning at least one of the objectives to the autonomous vehicle based on the location of the autonomous vehicle. The method also includes sending instructions to the autonomous vehicle in order to cause the autonomous vehicle to complete the at least one objective and after sending, tracking a status of the scouting quest.

Managing and tracking scouting tasks using autonomous vehicles

Aspects of the disclosure relate to routing an autonomous vehicle. For instance, the vehicle may be maneuvered along a route in a first lane using map information identifying a first plurality of nodes representing locations within the first lane and a second plurality of nodes representing locations within a second lane different from the first lane. While maneuvering, when the vehicle should make a lane change may be determined by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes. The assessment may be used to make the lane change from the first lane to the second lane.

Real-time lane change selection for autonomous vehicles

Aspects of the disclosure provide for identifying problematic areas within a service area for an autonomous vehicle transportation service. For instance, a starting location within the service area corresponding to a potential pickup location for passengers or cargo for the service may be identified. A destination within the service area may be identified. A simulation may be run in order to determine a route for a simulated vehicle to travel between the starting location and the destination. That the route includes a particular type of maneuver may be determined. A new simulation without allowing the simulated vehicle to complete the particular type of maneuver may be run. Whether the simulated vehicle reaches the destination in the new simulation may be determined. Based on the determination of whether the simulated vehicle reaches the destination in the new simulation, the starting location and destination location may be flagged as potentially problematic areas.

Stranding and scoping analysis for autonomous vehicle services

The technology relates to route planning and performing driving operations in autonomous vehicles, such as cargo trucks, articulating buses, as well as other vehicles. A detailed kinematic model of the vehicle in evaluated in conjunction with roadgraph and other information to determine whether a route or driving operation is feasible for the vehicle. This can include evaluating a hierarchical set of driving rules and whether current driving conditions impact any of the rules. Driving trajectories and cost can be evaluated when pre-planning a route for the vehicle to follow. This can include determining an ideal trajectory for the vehicle to take a particular driving action. Pre-planned routes may be shared with a fleet of vehicles, and can be modified based on information obtained by different vehicles of the fleet.

Kinematic model for autonomous truck routing

Aspects of the disclosure relate to pre-computing routes for autonomous vehicles using map shards. For example, a shard from a plurality of shards of a map may be selected. Each shard including a plurality of nodes and edges connecting pairs of nodes of the plurality of nodes, and each node of the plurality represents a location. A plurality of port nodes for the shard are identified. Each port node has an edge that enters into the selected shard or exists the selected shard. For each port node of the plurality having an edge that enters into the selected shard, optimal routes to each other port node of the plurality having an edge that exits the selected shard may be determined. The optimal routes for the selected shard may be sent to the autonomous vehicles in order to enable the autonomous vehicles to use the optimal routes to determine routes.

Pre-computing routes for autonomous vehicles using map shards

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.

Service area maps for autonomous vehicles

A method is provided for managing and tracking scouting tasks to obtain map information using a fleet of autonomous vehicles. For instance, the method includes defining a scouting quest to obtain the map information. The scouting quest includes a plurality of objectives. Each objective is associated with a geographic location from which sensor data is to be captured. The method also includes receiving a first update message from an autonomous vehicle of the fleet. The update message identifies a location of the autonomous vehicle. The method also includes assigning at least one of the objectives to the autonomous vehicle based on the location of the autonomous vehicle. The method also includes, sending instructions to the autonomous vehicle in order to cause the autonomous vehicle to complete the at least one objective and after sending, tracking a status of the scouting quest.

Growing community of inventors

Los Altos, CA, United States of America

Austin Daniel Abrams