提出更精准的车辆泊车横向速度模型，提升低速停车定位精度。
Lateral Velocity Model for Vehicle Parking Applications
- 基于实测数据重构泊车时横向运动模型
- 仅用两个参数即显著提升横向速度估计准确率
- 适合嵌入消费级车辆自动驾驶系统
自动泊车需要高精度定位以实现紧凑空间内的快速精准操作。虽然纵向速度可通过轮速编码器测量，但横向速度因缺乏专用传感器而难以估计。现有方法多依赖零滑移模型（即后轴无侧向速度），但该假设在低速行驶时并不成立，研究者常引入额外经验规则进行修正。本文分析真实泊车场景数据，发现零滑移假设存在系统性偏差，给出物理解释，并提出一种改进的横向速度模型，能更好捕捉泊车过程中的侧向动态。该模型仅需两个参数，显著提升估计精度，适合集成于消费级车辆应用中。
原文摘要 · Abstract (English)
Automated parking requires accurate localization for quick and precise maneuvering in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estimation of the lateral velocity remains a key challenge due to the absence of dedicated sensors in consumer-grade vehicles. Existing approaches often rely on simplified vehicle models, such as the zero-slip model, which assumes no lateral velocity at the rear axle. It is well established that this assumption does not hold during low-speed driving and researchers thus introduce additional heuristics to account for differences. In this work, we analyze real-world data from parking scenarios and identify a systematic deviation from the zero-slip assumption. We provide explanations for the observed effects and then propose a lateral velocity model that better captures the lateral dynamics of the vehicle during parking. The model improves estimation accuracy, while relying on only two parameters, making it well-suited for integration into consumer-grade applications.
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。