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cs.CV2026

SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning

Sviatoslav Voloshyn, Bruno K. W. Martens, Wangxin Liu +2

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 fr…

cs.CV2026

QueryOcc: Query-based Self-Supervision for 3D Semantic Occupancy

Adam Lilja, Ji Lan, Junsheng Fu +1

Learning 3D scene geometry and semantics from images is a core challenge in computer vision and a key capability for autonomous driving. Since large-scale 3D annotation is prohibit…

cs.CV2026

Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping

Chouaib Bencheikh Lehocine, Adam Lilja, Junsheng Fu +1

Online map estimation is a crucial component of autonomous driving systems that reduces the reliance on costly high-definition maps. State-of-the-art (SOTA) methods commonly predic…

cs.CV2025

Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition

Xin Bi, Zhichao Li, Yuxuan Xia +4

Accurate online map matching is fundamental to vehicle navigation and the activation of intelligent driving functions. Current online map matching methods are prone to errors in co…

cs.CV2025

Exploring Semi-Supervised Learning for Online Mapping

Adam Lilja, Erik Wallin, Junsheng Fu +1

The ability to generate online maps using only onboard sensory information is crucial for enabling autonomous driving beyond well-mapped areas. Training models for this task -- pre…

cs.CV2025

GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving

William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling +6

Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance…