Uatc, LLC

Pepsico, Inc.

Uber Technologies, Inc.

Aurora Operations, Inc.

Wei Liu

David McAllister Bradley

Colin Jeffrey Green

Michael Lee Phillips

Anthony Joseph Stentz

Lee Matthew Nicholson

Eric Michael Perko

Eric Lloyd Wilkinson

Kalin Vasilev Gochev

Samuel Philip Marden

Don Burnette

Somchaya Liemhetcharat

Vijay Subramanian

Harishma Dayanidhi

Peter S Given

Prasad V Joshi

Denise H Lefebvre

Marcin Jankowiak

Frank Schloss

Jason Haslow

Manish Marotrao Pande

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

Discrete decision architecture for motion planning system of an autonomous vehicle

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.

Autonomous vehicles featuring machine-learned yield model

Cans, bottles and/or other containers used to hold a carbonated beverage can include internal features to promote and/or control bubble formation.

Control of bubble size in a carbonated liquid

A thermoplastic article includes one or more recyclable-friendly synthetic coloring pigments. The pigment has a melting temperature that is about 20 to about 50&#xb0; C. higher than that of the base polymer. This enables the pigment to remain predominately intact (e.g., unmelted) during injection molding and act as a colorant in the preform and resulting blow molded container. When the container is remelted during subsequent processing, such as post-consumer recycling, the pigment melts and transesterifies into the base polymer. Thus, upon such further processing, the pigment effectively is subsumed into the base polymer and does not affect the basic characteristics and properties of the base polymer.

Recyclable colorants in plastic beverage containers

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Pittsburgh, PA, United States of America

Wei Liu