Generative adversarial network enriched driving simulation

Generative adversarial network enriched driving simulation

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real t...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Deng, Nengxiu, Xiao, Sinan, Qin, Tao, Lou, Tiancheng, Yu, Xiang, Zhang, Yubo, Song, Hao, Peng, Jun, Li, Tianyi
Format: Patent
Sprache:eng
Schlagworte:
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
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TRANSPORTING
VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.