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An accurate vehicle dynamic model is the key to bridge the gap betweensimulation and real road test in autonomous driving. In this paper, we presenta Dynamic model-Residual correction model Framework (DRF) for vehicle dynamicmodeling. On top of any existing open-loop dynamic model, this framework buildsa Residual Correction Model (RCM) by integrating deep Neural Networks (NN) withSparse Variational Gaussian Process (SVGP) model. RCM takes a sequence ofvehicle control commands and dynamic status for a certain time duration asmodeling inputs, extracts underlying context from this sequence with deepencoder networks, and predicts open-loop dynamic model prediction errors. Fivevehicle dynamic models are derived from DRF via encoder variation. Ourcontribution is consolidated by experiments on evaluation of absolutetrajectory error and similarity between DRF outputs and the ground truth.Compared to classic rule-based and learning-based vehicle dynamic models, DRFaccomplishes as high as 74.12% to 85.02% of absolute trajectory error dropamong all DRF variations.

DRF: A Framework for High-Accuracy Autonomous Driving Vehicle Modeling