Baidu USA LLC

Apollo Autonomous Driving USA LLC

Baidu.com Times Technology (Beijing) Co. Ltd.

Yu Cao

Jiangtao Hu

Jingao Wang

Qi Luo

Jinghao Miao

Yu Wang

Shu Jiang

Jinyun Zhou

Kecheng Xu

Jiaxuan Xu

Hongyi Sun

Weiman Lin

Xiangquan Xiao

Zejun Lin

Wesley Reynolds

Longtao Lin

Wei Wang

Szu Hao Wu

Ang Li

Zongbao Feng

A system generates a first variant of a first machine learning (ML) model, the first variant associated with an initial hyperparameter value. The system determines a prediction metric for the first variant of the first ML model, the prediction metric indicating an accuracy of the behavior prediction. The system generates an estimated simulation metric for the first variant of the first ML model by applying a second ML model to the prediction metric. The system identifies a first hyperparameter associated with a second variant of the first ML model, the second variant of the first ML model having a corresponding prediction metric and a corresponding estimated simulation metric that meet a first predetermined criteria, the second variant of the first ML model is used by an autonomous driving vehicle (ADV) for behavior prediction.

Multi-level optimization framework for behavior prediction in autonomous driving

According to various embodiments, the disclosure discloses systems, methods and media for formulating training datasets for learning-based components in an autonomous driving vehicle (ADV). In an embodiment, an exemplary method includes allocating training datasets for training a learning-based model in the ADV, each training dataset being allocated to one of multiple predefined driving scenarios; determining a weight of each training dataset out of the training datasets; and optimizing the weight of each training dataset in one or more iterations according to a predetermined algorithm until a performance of the learning-based model reaches a predetermined threshold. The predetermined algorithm is one of a random search algorithm, a grid search algorithm, or a Bayesian algorithm.

Scenario-based training data weight tuning for autonomous driving

Growing community of inventors

Sunnyvale, CA, United States of America

Yu Cao