SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
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 rea
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorSviatoslav Voloshyn →
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
- Linked via arxiv authorBruno K. W. Martens →
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
- Linked via arxiv authorWangxin Liu →
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
- Linked via arxiv authorJakob Vinkås →
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
- Linked via arxiv authorJunsheng Fu →
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
