DRF provides a general accuracy improvement framework on existing vehicle dynamic models. On top of any existing open-loop dynamic model, this framework builds a Residual Correction Model (RCM) by integrating deep Neural Networks (NN) with Stochastic Variational Gaussian Process (SVGP) model.
Nov 1, 2020 · In this paper, we present a Dynamic model-Residual correction model Framework (DRF) for vehicle dynamic modeling.
Oct 1, 2021 · Compared to classic rule-based and learning-based vehicle dynamic models, DRF accomplishes as high as 74.12% to 85.02% of the absolute ...
Sep 27, 2021 · DRF provides a general accuracy improvement framework on existing vehicle dynamic models. On top of any existing open-loop dynamic model, this ...
The results illustrate the advantages of the proposed MO-API method under different traffic conditions. Furthermore, we also tested the learned decision policy ...
Nov 11, 2020 · An accurate vehicle dynamic model is the key to bridge the gap between simulation and real road test in autonomous driving.
Nov 1, 2020 · This paper introduces a highly automated learning-based vehicle dynamic modeling procedure, which has been deployed on Baidu Apollo self-driving ...
A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving. S. Jiang, Y. Wang, W. Lin, Y. Cao, L. Lin, J. Miao, and Q. Luo.
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This paper reviews open-source and commercial AD frameworks and simulators, introducing and comparing their features, functionalities, and so on.
accurate predictions. 172. 3 Shared autonomous vehicle framework. 173. This section presents a general framework for dynamic simulation of SAVs to admit the.