Uatc, LLC

Agr International, Inc.

Jeff Schneider

William E Schmidt

Georg V Wolfe

Nemanja Djuric

Tsung-Han Lin

Vladan Radosavljevic

Thi Duong Nguyen

Henggang Cui

Fang-Chieh Chou

Tzu-Kuo Huang

Richard Brian Donnelly

Bryan John Nagy

Xiaodong Zhang

Yifang Liu

Andrew Raymond Sturges

Alexander Edward Chao

Collin Christopher Otis

David McAllister Bradley

Anthony Joseph Stentz

Galen Clark Haynes

Micol Marchetti-Bowick

Tian Lan

Alexander David Styler

Neil Traft

Ian Dewancker

Huahua Wang

Systems and methods control the operation of a blow molder. An indication of a crystallinity of at least one container produced by the blow molder may be received along with a material distribution of the at least one container. A model may be executed, where the model relates a plurality of blow molder input parameters to the indication of crystallinity and the material distribution and where a result of the model comprises changes to at least one of the plurality of blow molder input parameters to move the material distribution towards a baseline material distribution and the crystallinity towards a baseline crystallinity. The changes to the at least one of the plurality of blow molder input parameters may be implemented.

Blow molder control systems and methods

An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Determining autonomous vehicle routes

Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

Object motion prediction and autonomous vehicle control

Systems, methods, tangible non-transitory computer-readable media, and devices associated with the motion prediction and operation of a device including a vehicle are provided. For example, a vehicle computing system can access state data including information associated with locations and characteristics of objects over a plurality of time intervals. Trajectories of the objects at subsequent time intervals following the plurality of time intervals can be determined based on the state data and a machine-learned tracking and kinematics model. The trajectories of the objects can include predicted locations of the objects at subsequent time intervals that follow the plurality of time intervals. Further, the predicted locations of the objects can be based on physical constraints of the objects. Furthermore, indications, which can include visual indications, can be generated based on the predicted locations of the objects at the subsequent time intervals.

Motion prediction for autonomous devices

Systems and methods for controlling the operation of a blow molder are disclosed. An indication of a crystallinity of at least one container produced by the blow molder may be received along with a material distribution of the at least one container. A model may be executed, where the model relates a plurality of blow molder input parameters to the indication of crystallinity and the material distribution and where a result of the model comprises changes to at least one of the plurality of blow molder input parameters to move the material distribution towards a baseline material distribution and the crystallinity towards a baseline crystallinity. The changes to the at least one of the plurality of blow molder input parameters may be implemented.

A method for determining a canonical route includes receiving trip data associated with one or more traversals of a plurality of roadways in a geographic location by one or more autonomous vehicles. The method includes generating at least one canonical route based on the trip data, wherein the at least one canonical route includes at least one roadway connected with another roadway in the plurality of roadways. The method includes providing canonical route data associated with the at least one canonical route to an autonomous vehicle for controlling travel of the autonomous vehicle on the at least one canonical route.

Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a predicted trajectory of the object.

The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Machine learning for predicting locations of objects perceived by autonomous vehicles

Growing community of inventors

Pittsburgh, PA, United States of America

Jeff Schneider