Gm Cruise Holdings LLC

Michael Rusignola

Brian Donohue

Chen Xie

Juan Fasola

Divya Thakur

Charles Bruce Matlack

Sarah M Rizk

Lucio Rech

Stephen Worlow

Daniel Blick

Varun Bharadwaj

Zachary Garcia

Matthew Fox

Rohan Agrawal

Armin Mahmoudi

Christopher Brian Roland

Steve Xie

Katherine Anne Fotion

Nick Tran

Anmol Garg

Riccardo Pavesi

Alexander Sennikov

Andra-Maria Bogildea

Christopher Setian

Hakan Tunc

Irfan Lone

Alexander H Case

John Habeb Khawam

Anusha Sridhar Rao

Zachary Wright Garcia

Systems and techniques are provided for selecting and dispatching an autonomous vehicle (AV) in a set of AVs based on resource usage predictions. An example process includes receiving, from a user device, a ride request for a ride between a pick-up location and a drop-off location and determining, for each AV in a set of AVs, a predicted resource usage for a completion of the ride based on a distance between the pick-up location and the drop-off location, a distance between an AV location and the pick-up location, one or more environmental factors, and one or more vehicle-specific factors. The example process further includes selecting an AV in the set of AVs for the ride based on the predicted resource usage for the completion of the ride and a maintenance state of each AV.

Autonomous vehicle dispatch based on resource usage predictions

The present technology can detect potential labeling conflicts made during different labeling tasks when those tasks are checked in to ensure that a map database is free from conflicts that might interrupt publishing of a map. The present technology can determine whether a first labeled version of map portion is based on a most recent version for the map portion that is stored in the map database, and when it is determined that it is not, and there is an intervening version, the present technology can identify differences between the intervening version of the map portion and the first version of the map portion and identify differences between the second version of the map portion and the first version of the map portion. Based on the identified differences, the present technology can determine whether any of those differences are conflicting.

Difference merging for map portions

Systems, methods, and devices are disclosed for mapping historical information about behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. Based on the mapped historical information, a prediction is determined about a behavior of an object proximate to an autonomous vehicle at the location, where the prediction is based on a statistical analysis of the historical information that is applied to the object. One or more behaviors of the AV are affected based on the prediction.

Map prior layer

Systems and methods provide for routing an autonomous vehicles based on an assessment of its limitations. The autonomous vehicle can avoid problematic routes by applying a set of rules to a map, where the set of rules are associated with a feature that causes a failure of the autonomous vehicle due to an autonomous vehicle limitation. Based on the application of the set of rules, a portion of a route on the map that is associated with the feature can be identified, and an identifier can be applied to the identified portion of the route associated with the feature on a routing map. The portion of the route can be omitted from a routable graph applied to the routing map, such that the route for the autonomous vehicle is generated in accordance with the routable graph.

Programmatic application of router flags for vehicle limitations

Systems and methods provide for routing autonomous vehicles by simulating an autonomous vehicle traversing a route on a map representing a physical area. A router flag can be generated at a specific location on the map. A router flag rule that defines a factor that caused a failure of the autonomous vehicle can then be created at the location, and the autonomous vehicle can be simulated traversing the to identify at least one other area on the map where the autonomous vehicle is likely to fail. An identifier can be applied to the other area on the map where the autonomous vehicle is likely to fail according to the router flag rule. The other area with the applied identifier can be omitted from a routable graph applied to the routing map, such that the route for the autonomous vehicle is generated in accordance with the routable graph.

The present technology provides a system that can update aspects of an authoritative map portion stored on an autonomous vehicle using low-resolution data from the at least one sensor of the autonomous vehicle, and therefore avoids the need for dispatching the special purpose mapping vehicle for these updates. The captured low-resolution data can be compared with the high-resolution map portion to determine that the sensor data reflects a feature in a way that is inconsistent with how that feature is represented in the authoritative map portion. The sensor data can then be used to relabel the authoritative map portion.

Map updates based on data captured by an autonomous vehicle

Growing community of inventors

Burlingame, CA, United States of America

Michael Rusignola