Lateral Velocity Model for Vehicle Parking Applications

TL;DR

This paper introduces a new lateral velocity model for vehicle parking that improves localization accuracy by better capturing vehicle dynamics, addressing limitations of existing simplified models, and is suitable for consumer-grade applications.

Contribution

It proposes a novel lateral velocity model based on real-world parking data that outperforms zero-slip assumptions with only two parameters.

Findings

The model accurately captures lateral dynamics during parking.

It significantly improves lateral velocity estimation accuracy.

The model is simple and suitable for consumer-grade vehicles.

Abstract

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.