arXiv:2608. 09610v1 Announce Type: cross Abstract: Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions.
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Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions. While anchor-based detectors provide efficient candidate generation, their performance is limited by two coupled issues: backbone features often lose structural continuity along partially visible lanes, and classification confidence may decouple from line-level localization quality, allowing inaccurate anchors to persist before non-maximum suppression (NMS).
arXiv:2610.09118v1 Announce Type: new Abstract: Topology reasoning jointly detects 3D lanes and traffic elements from multi-view images and infers their structural connectivity. Current methods model...
Autonomous driving requires understanding the road as a graph of drivable lanes and their connectivity, beyond the ego lane alone, to follow routes through intersections and reason about cross- and merging-traffic. Recent perception models infer such lane topology, i.
arXiv:2608. 16338v1 Announce Type: cross Abstract: Video lane detection requires predictions that remain stable across frames, yet severe vehicle occlusions can break temporal cues.
The paper introduces Adaptive Dual-Constrained Line Aggregation (ADLA), a framework that extracts line segments across multiple detection paradigms by aggregating pixels from an edge strength map under orientation coherence and bounded orthogonal distance constraints. ADLA dynamically updates line centroids and orientations, incorporates edge strength into various stages, and reduces parameter tuning. Experiments on generic, wireframe, and Manhattan line segment datasets show strong performance, achieving F^H scores of 0.8665, 0.8720, and 0.7297 respectively.
The paper surveys one‑stage object detectors for autonomous driving, covering the evolution from early models like YOLOv1 and SSD to recent real‑time architectures such as YOLOv10 and anchor‑free detectors like FCOS and CenterNet. It compares these methods on design choices, feature‑fusion strategies, loss functions, deployment trade‑offs, and benchmark performance, while also summarizing datasets, evaluation metrics, open challenges, and future research directions. The survey emphasizes how one‑stage detectors balance speed, accuracy, efficiency, and robustness, noting the gap between benchmark results and dependable real‑world performance.