{"ID":5554312,"CreatedAt":"2026-07-02T02:11:27.934456424Z","UpdatedAt":"2026-07-04T16:18:06.013463601Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01139","arxiv_id":"2607.01139","title":"SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning","abstract":"This paper presents SD-RouteFusion, a deployable end-to-end ego-trajectory prediction method that fuses a front-facing camera, vehicle kinematics, and a navigation route derived from a Standard Definition (SD) map. Unlike approaches that rely on High Definition (HD) map geometry, SD-RouteFusion aligns the learning objective with scalable and production-ready SD-map route inputs, enabling route-aware prediction without requiring HD-map infrastructure. First, we demonstrate that SD-map route prior provides a powerful long-horizon semantic prior. Through a comprehensive study on a large-scale real-world dataset comprising 480k driving scenarios across 10 European countries and the U.S., we quantify the value of SD-route conditioning: incorporating SD-map routes yields a 10.5% ADE improvement over an image-and-kinematics baseline, while our full fusion strategy achieves a 16.9% ADE reduction given a prediction horizon of 8 seconds. The fusion strategy consists of a dual-hypothesis design paired with a gated classifier, to ensure robustness under route corruption and visual uncertainty. Finally, to support broader evaluation, we release an SD-route generation toolkit that enables SD-route-conditioned ego-trajectory prediction on all datasets containing ego pose and future trajectories. Together, SD-RouteFusion establishes a practical path toward robust, route-aware ego-trajectory prediction at scale.","short_abstract":"This paper presents SD-RouteFusion, a deployable end-to-end ego-trajectory prediction method that fuses a front-facing camera, vehicle kinematics, and a navigation route derived from a Standard Definition (SD) map. Unlike approaches that rely on High Definition (HD) map geometry, SD-RouteFusion aligns the learning obje...","url_abs":"https://arxiv.org/abs/2607.01139","url_pdf":"https://arxiv.org/pdf/2607.01139v1","authors":"[\"Sviatoslav Voloshyn\",\"Bruno K. W. Martens\",\"Wangxin Liu\",\"Jakob Vinkås\",\"Junsheng Fu\"]","published":"2026-07-01T16:22:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}