Gm Cruise Holdings LLC

Zachary Garcia

Chen Xie

Rohan Agrawal

Divya Thakur

Nestor Grace

Michael Rusignola

Changkai Zhou

Matthew Fox

Katherine Anne Fotion

Nick Tran

Kevin Yuwen Tong

Chen “Steve” Xie

The disclosed technology provides solutions for determining, by an AV, whether a roadway is closed by determining and understanding the intent of the emergency vehicle. A method comprises navigating an autonomous vehicle (AV) along a roadway; receiving road data, by the AV, wherein the road data corresponds with at least one emergency vehicle; identifying the at least one emergency vehicle; determining a pose of the at least one emergency vehicle in relation to the roadway; and determining if the roadway is closed based on the pose of the emergency vehicle in relation to the roadway. Systems and machine-readable media are also provided.

Method for identification of emergency vehicle road closures

Systems, methods, and devices are disclosed for predicting creep behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. An autonomous vehicle located at a first road segment connecting to an intersection having a stop sign can detect a first vehicle approaching the intersection from a second road segment connecting to the intersection. Using a model indicating an average creep point location specific to the second road segment connecting to the intersection, the autonomous vehicle can predict that the first vehicle will yield at the average creep point location that is specific to the second road segment.

Human-like autonomous vehicle behavior based on map prior layer

The present disclosure provides a method comprising receiving by an autonomous vehicle ('AV') service provider a request for an AV service from a user, wherein the request identifies an origin and a destination for the AV service; identifying a plurality of possible routes between the origin and the destination; and, for each one of the plurality of possible routes assigning to the one of the possible routes at least one of a comfort score, a confidence score, and a risk an risk score based on data associated with the one of the possible routes determining a price for the one of the possible routes based on a combination of the at least one of the comfort score, the confidence score, and the risk score assigned to the one of the possible routes.

System for dynamic autonomous vehicle service pricing

The present technology pertains to visualization of maps used for autonomous vehicle applications. These maps include many layers of dense information. Each layer can be stored in a vector format and small portions, called tiles, of a map can be requested by a users web browser. The web browser can filter for desired layers and render all desired map layers at once to provide visualization of map and layers of interest.

Dynamic map rendering

Systems, methods, and devices are disclosed for predicting yield behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. An autonomous vehicle located at a first road segment connecting to an intersection having a stop sign can detect a first vehicle approaching the intersection from a second road segment connecting to the intersection. Using a model indicating an average yield location and yield time where both are specific to the second road segment connecting to the intersection, the autonomous vehicle can predict that the first vehicle will yield at the average yield location that is specific to the second road segment.

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, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

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San Francisco, CA, United States of America

Zachary Garcia