DRF: A Framework for High-Accuracy Autonomous Driving Vehicle Modeling
This addresses the need for more accurate simulation-to-real-world bridging in autonomous driving, though it appears incremental as it builds on existing dynamic models.
The paper tackles the problem of improving vehicle dynamic modeling accuracy for autonomous driving by proposing a Dynamic model-Residual correction model Framework (DRF), which reduces absolute trajectory error by 74.12% to 85.02% compared to existing models.
An accurate vehicle dynamic model is the key to bridge the gap between simulation and real road test in autonomous driving. In this paper, we present a Dynamic model-Residual correction model Framework (DRF) for vehicle dynamic modeling. On top of any existing open-loop dynamic model, this framework builds a Residual Correction Model (RCM) by integrating deep Neural Networks (NN) with Sparse Variational Gaussian Process (SVGP) model. RCM takes a sequence of vehicle control commands and dynamic status for a certain time duration as modeling inputs, extracts underlying context from this sequence with deep encoder networks, and predicts open-loop dynamic model prediction errors. Five vehicle dynamic models are derived from DRF via encoder variation. Our contribution is consolidated by experiments on evaluation of absolute trajectory error and similarity between DRF outputs and the ground truth. Compared to classic rule-based and learning-based vehicle dynamic models, DRF accomplishes as high as 74.12% to 85.02% of absolute trajectory error drop among all DRF variations.