{"ID":2846584,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01369","arxiv_id":"2511.01369","title":"Lateral Velocity Model for Vehicle Parking Applications","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.01369","url_pdf":"https://arxiv.org/pdf/2511.01369v1","authors":"[\"Luis Diener\",\"Jens Kalkkuhl\",\"Markus Enzweiler\"]","published":"2025-11-03T09:11:34Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}