An Automated Learning-Based Procedure for Large-scale Vehicle Dynamics Modeling on Baidu Apollo Platform
Jiaxuan Xu, Qi Luo, Kecheng Xu, Xiangquan Xiao, Siyang Yu, Jiangtao Hu, Jinghao Miao, Jingao Wang
Abstract
In the autonomous driving industry, vehicle dynamic models are important to control-in-the-loop simulations. For current commercial self-driving simulators, vehicle dynamic models are expressed explicitly by sophisticated analytical equations, which are accurate but difficult to build and expensive to scale to fleets of vehicles of different brands. In this paper, we introduce a highly automated learning-based vehicle dynamic modeling procedure, which has been deployed on Baidu Apollo self-driving platform, to support cross-vehicle data-driven applications on a large scale. Compared with our previous analytical models, the end-to-end learning-based dynamic models can achieve high accuracy with significantly reduced re-development effort.
BibTeX
@inproceedings{iros2019_anautomatedlearn,
title = {An Automated Learning-Based Procedure for Large-scale Vehicle Dynamics Modeling on Baidu Apollo Platform},
author = {Jiaxuan Xu and Qi Luo and Kecheng Xu and Xiangquan Xiao and Siyang Yu and Jiangtao Hu and Jinghao Miao and Jingao Wang},
booktitle = {IROS 2019},
year = {2019}
}