A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving

Abstract

Vehicle dynamic models are the key to bridge the gap between simulation and real road test in autonomous driving. An accurate vehicle model allows control algorithms in simulation being transferred to real road test with same quality. In this paper, we present a dynamic model residual correction framework (DRF) for vehicle dynamic modeling. 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. RCM takes a sequence of vehicle control commands and dynamic states 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 variations. Our contribution is consolidated with evaluation of the absolute trajectory error and the 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 the absolute trajectory error drop among all DRF variations.