用数据驱动方法建模车辆动态，无需物理结构信息
VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
- 基于门控循环单元的轻量编码解码模型，从车载数据预测未来状态
- 极端工况下平均相对误差低于2.6%，噪声下仍保持稳定
- 完全数据驱动，输出自然符合物理规律，适合自动驾驶应用
为高性能车辆建立动态模型是一项复杂任务，通常需要详细的系统结构信息。然而在自动驾驶开发中，这些信息往往不可得，导致建模面临信息匮乏的挑战。本文提出一种基于门控循环单元（GRU）的轻量级编码解码模型，通过车载测量的历史状态和驾驶员控制动作，预测车辆未来状态。实验表明，该模型在极端动态条件下最大平均相对误差低于2.6%，且对感兴趣频段内的噪声输入具有良好的鲁棒性。由于完全数据驱动且无物理约束，模型输出的纵向与侧向加速度、偏航率及纵向速度等信号表现出良好的物理一致性。
原文摘要 · Abstract (English)
Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder model based on Gate Recurrent Unit layers to correlate the vehicle's future state with its past states, measured onboard, and control actions the driver performs. The results demonstrate that the model achieves a maximum mean relative error below 2.6% in extreme dynamic conditions. It also shows good robustness when subject to noisy input data across the interested frequency components. Furthermore, being entirely data-driven and free from physical constraints, the model exhibits physical consistency in the output signals, such as longitudinal and lateral accelerations, yaw rate, and the vehicle's longitudinal velocity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。