论文标题
DRF：高准确自动驾驶车辆建模的框架
DRF: A Framework for High-Accuracy Autonomous Driving Vehicle Modeling
论文作者
论文摘要
准确的车辆动态模型是弥合自动驾驶中模拟和真实道路测试之间差距的关键。在本文中，我们提出了用于车辆动态建模的动态模型校正模型框架（DRF）。除了任何现有的开环动态模型之外，该框架还通过将深度神经网络（NN）与稀疏变异高斯过程（SVGP）模型集成来构建残差校正模型（RCM）。 RCM在一定时间持续时间内采用一系列车辆控制命令和动态状态，作为建模输入，通过深层编码网络从此序列中提取上下文，并预测开环动态模型预测错误。通过编码器变化从DRF得出了五个车辆动态模型。通过评估DRF输出与地面真相之间的绝对轨迹误差和相似性的实验，我们的贡献得到了整合。与经典的基于规则和基于学习的车辆动态模型相比，DRF在所有DRF变化中的绝对轨迹误差下降的高达74.12％至85.02％。
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.
