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.

翻译：自动泊车需要精确定位，以便在狭窄空间内实现快速且精确的操控。虽然纵向速度可通过车轮编码器测量，但由于消费级车辆缺乏专用传感器，横向速度的估计仍是一个关键挑战。现有方法通常依赖于简化的车辆模型，例如零滑移模型，该模型假设后轴无横向速度。众所周知，这一假设在低速行驶时不成立，因此研究人员引入了额外的启发式方法来解释差异。在本研究中，我们分析了泊车场景中的真实数据，并识别出与零滑移假设的系统性偏差。我们对观察到的效应提供了解释，随后提出了一种横向速度模型，该模型能更好地捕捉泊车过程中车辆的横向动力学特性。该模型仅依赖两个参数，提高了估计精度，使其非常适合集成到消费级应用中。