Invention Grant
- Patent Title: Machine learning for predicting locations of objects perceived by autonomous vehicles
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Application No.: US15684865Application Date: 2017-08-23
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Publication No.: US10579063B2Publication Date: 2020-03-03
- Inventor: Galen Clark Haynes , Ian Dewancker , Nemanja Djuric , Tzu-Kuo Huang , Tian Lan , Tsung-Han Lin , Micol Marchetti-Bowick , Vladan Radosavljevic , Jeff Schneider , Alexander David Styler , Neil Traft , Huahua Wang , Anthony Joseph Stentz
- Applicant: UATC, LLC
- Applicant Address: US CA San Francisco
- Assignee: UATC, LLC
- Current Assignee: UATC, LLC
- Current Assignee Address: US CA San Francisco
- Agency: Dority & Manning, P.A.
- Main IPC: G01C21/20
- IPC: G01C21/20 ; G08G1/16 ; G05D1/02 ; G01C21/34 ; B60W30/00

Abstract:
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.
Public/Granted literature
- US20190025841A1 Machine Learning for Predicting Locations of Objects Perceived by Autonomous Vehicles Public/Granted day:2019-01-24
Information query
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