Autonomous racing is gaining attention for its potential to advance autonomous vehicle technologies. Accurate race car dynamics modeling is essential for capturing and predicting future states like position, orientation, and velocity. However, accurately modeling complex subsystems such as tires and suspension poses significant challenges. In this paper, we introduce the Deep Kernel-based Multi-task Gaussian Process (DKMGP), which leverages the structure of a variational multi-task and multi-step Gaussian process model enhanced with deep kernel learning for vehicle dynamics modeling. Unlike existing single-step methods, DKMGP performs multi-step corrections with an adaptive correction horizon (ACH) algorithm that dynamically adjusts to varying driving conditions. To validate and evaluate the proposed DKMGP method, we compare the model performance with DKL-SKIP and a well-tuned single-track model, using high-speed dynamics data (exceeding 230kmph) collected from a full-scale Indy race car during the Indy Autonomous Challenge held at the Las Vegas Motor Speedway at CES 2024. The results demonstrate that DKMGP achieves upto 99% prediction accuracy compared to one-step DKL-SKIP, while improving real-time computational efficiency by 1752x. Our results show that DKMGP is a scalable and efficient solution for vehicle dynamics modeling making it suitable for high-speed autonomous racing control.

翻译：自动驾驶赛车因其推动自动驾驶技术发展的潜力而日益受到关注。精确的赛车动力学建模对于捕捉和预测位置、朝向和速度等未来状态至关重要。然而，对轮胎和悬架等复杂子系统进行精确建模存在显著挑战。本文提出基于深度核的多任务高斯过程（DKMGP），该方法利用变分多任务多步高斯过程模型的结构，并通过深度核学习增强，用于车辆动力学建模。与现有的单步方法不同，DKMGP采用自适应校正视界（ACH）算法进行多步校正，该算法能动态适应变化的驾驶条件。为验证和评估所提出的DKMGP方法，我们使用在2024年国际消费电子展期间于拉斯维加斯赛车场举行的Indy自动驾驶挑战赛中，从全尺寸Indy赛车上采集的高速动力学数据（超过230公里/小时），将模型性能与DKL-SKIP以及经过良好调校的单轨模型进行比较。结果表明，与单步DKL-SKIP相比，DKMGP实现了高达99%的预测精度，同时实时计算效率提升了1752倍。我们的研究证明，DKMGP是一种可扩展且高效的车辆动力学建模解决方案，适用于高速自动驾驶赛车控制。