Neural network approach for parameter learning to speed up planning for complex driving scenarios

In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision...

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Bibliographic Details
Main Authors Zhou, Jinyun, Xu, Jiaxuan, Luo, Qi, Hu, Jiangtao, He, Runxin, Wang, Yu, Jiang, Shu, Miao, Jinghao
Format Patent
LanguageEnglish
Published 22.08.2023
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
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TRANSPORTING
VEHICLES IN GENERAL
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Summary:In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.
Bibliography:Application Number: US201916399538