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arXiv:2608. 16480v1 Announce Type: cross Abstract: We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequences.
arXiv:2608.13147v2 Announce Type: replace Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
The paper introduces a multi‑modal traffic sign detection framework that fuses camera and LiDAR data using an Intensity‑Aware Deformable Fusion module to align retro‑reflective LiDAR cues with visual features. It also presents a dual motion‑model tracker to handle non‑linear perspective changes and a semantic attribute classification pipeline that estimates occlusion, readability, sign embeddedness, and road relevance. Evaluated on a dataset covering more than 60 countries and 2,500 hours of driving, the system achieves an Object Miss Ratio of 0.49% across 221,068 sequences, indicating strong global generalization for autonomous driving.
NeuroSymbEAD is a large‑scale neuro‑symbolic caption dataset that builds an ego‑centric knowledge graph of static and dynamic objects on the KITTI‑360 dataset, annotating classes, categories, heading directions, orientations, and distances from the ego‑vehicle. The dataset generates multilevel textual captions that serve as a lightweight representation of an ego‑centric scene map, enabling outdoor scene‑map reconstruction, visual recognition, and object grounding. Baselines for driving common sense and traffic/scene understanding are established, and the dataset is benchmarked using pre‑trained grounding and learned auto‑regressive captioning networks to support vision‑language and foundation models for traffic‑scene explanation, 3D reasoning, and interpretable autonomous‑driving perception.
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.