Neural network based modeling and simulation of non-stationary traffic objects for testing and development of autonomous vehicle systems

A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle i...

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Bibliographic Details
Main Author Anthony, Samuel English
Format Patent
LanguageEnglish
Published 03.10.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION
CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES
CONTROLLING
COUNTING
PERFORMING OPERATIONS
PHYSICS
REGULATING
ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT
SIGNALLING
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TRAFFIC CONTROL SYSTEMS
TRANSPORTING
VEHICLES IN GENERAL
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Summary:A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.
Bibliography:Application Number: US201916709790