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.
By Chenguang Liu, Chisheng Wang, Huilin Chen, Chuanhua Zhu, Qingquan Li
The paper proposes a method for identifying the current traffic situation of a vehicle using Bird's‑Eye‑View images. It combines object detection with semantic segmentation and a situation identification neural network built on the Cam2BEV projective transformation. The approach was validated in the CARLA simulator and on Cityscapes and nuScenes datasets, demonstrating that it can prioritize relevant sensor data for efficient distribution in connected vehicles.
By Falk Dettinger, Akshay Narla, Michael Weyrich
The paper introduces a geo‑referenced smartphone dashcam dataset of 63,839 frames from South Texas and a baseline framework for assessing road lane quality. Three new metrics—Contrast Stability Index (CSI), Line Continuity Ratio (LCR), and Regression Lane Confidence Score (RLCS)—are proposed to evaluate lane‑marking visibility, continuity, and map consistency without dense human labels. Baseline models achieve strong performance, and pilot validation shows the metrics correlate better with human judgments than generic image‑quality measures.
By Shaif Chowdhury, Akhila Yeruvasu, Moshiur Rahman Tonmoy, Ayush Goyal, Dunren Che
arXiv:2609.05523v1 Announce Type: cross
Abstract: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate...
By Akos T. Kopeczi-Bocz, Tian Mi, Gabor Orosz, Denes Takacs
The paper presents a low‑cost, self‑supervised method for detecting wheel tracks on snow‑covered roads using synchronized RGB and long‑wave infrared (RGB‑I) imagery. Training labels are automatically generated by projecting future wheel trajectories from GNSS data, and a neural network is trained to identify wheel tracks on featureless, white‑out road surfaces. Experimental results show the approach is effective in visually monotonous adverse environments, providing a robust alternative for winter autonomy. The ThermalTrack dataset is released for further research.
By Yiming Yang, Jeremy P. Bos
The paper introduces a multi‑camera computer vision system that computes Post‑Encroachment Time (PET) in real time to assess intersection safety. Using four synchronized cameras and YOLOv11 segmentation on NVIDIA Jetson AGX Xavier devices, vehicle detections are mapped to a unified bird’s‑eye view and processed with a pixel‑level PET algorithm to generate high‑resolution heatmaps. The system records PET data in an SQL database and demonstrates real‑time throughput (2.68 FPS) with 800 × 800 heatmaps, validating a scalable, decentralized approach for high‑resolution intersection safety evaluation.
By Shounak Ray Chaudhuri, Arash Jahangiri, Christopher Paolini
The paper explores the use of Closed‑Circuit Television (CCTV) footage to estimate urban rail platform crowding in real time. It compares three computer‑vision methods—object detection and counting, crowd‑level classification with a Vision Transformer, and semantic segmentation—to extract crowd-related features. A novel convex ridge regression technique is introduced to convert segmentation outputs into passenger counts, and the methods are evaluated on a privacy‑preserving dataset of over 600 hours of Washington Metropolitan Area Transit Authority (WMATA) video, showing that CCTV alone can provide valuable real‑time crowd estimates.
By Riccardo Fiorista, Awad Abdelhalim, Anson F. Stewart, Gabriel L. Pincus, Ian Thistle, Jinhua Zhao
arXiv:2610.33758v1 Announce Type: new
Abstract: Road infrastructure perception is a fundamental component of autonomous driving, as it recovers the structured road elements that define roadway geomet...
By Guoyang Zhao, Weiqing Qi, Kai Zhang, Zhihai Bi, Tianshuai Hu, Zeying Gong, Dezong Zhao, Jun Ma
YILDIZ-VPR is a new visual geo‑localization dataset collected by repeatedly walking across the Davutpasa campus of Yildiz Technical University. It offers dense coverage of outdoor scenes captured at various times of day, seasons, and weather conditions, featuring historical buildings, modern structures, roads, green areas, and wooded regions. Each GoPro 9 video is synchronized with GPS, gyroscope, speed, and temperature data, providing precise location labels for extracted frames.
By Serdar Yildiz, Abbas Memi\c{s}, Song\"ul Varli
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.
By Prakhar Bhardwaj, Simone Weikl, Kilian Mang, Elia Jonas Sandtner
arXiv:2608.31107v1 Announce Type: new
Abstract: The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalizatio...
By Lucas Wojcik, Gabriel E. Lima, Sergio M. Silva Jr., Eduil Nascimento Jr., David Menotti
The 10th AI City Challenge, held alongside ECCV 2026, celebrates a decade of benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 inception focused on vehicle detection, classification, and tracking, the challenge has expanded into a comprehensive benchmark suite covering multi‑camera perception, multimodal reasoning, synthetic‑to‑real learning, generative forecasting, and privacy‑preserving evaluation. The 2026 edition saw 325 registered teams from 26 countries, with six main tracks—spanning multi‑camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text‑based person anomaly search, generative traffic video forecasting, and cross‑city object detection—plus two out‑of‑domain leaderboards for fisheye traffic‑violation understanding and pedestrian situated‑intent VQA.
By Zheng Tang, Shuo Wang, David C. Anastasiu, Ming-Ching Chang, Anuj Sharma, Quan Kong, Munkhjargal Gochoo, Jun-Wei Hsieh, Tomasz Kornuta, Zhedong Zheng, Renran Tian, Judah Goldfeder, Fulgencio Navarro, Yuxing Wang, Yizhou Wang, Sameer Satish Pusegaonkar, Anqi Li, Nalin Dadhich, Ridham Kachhadiya, Dhanishtha Patil, Haoquan Liang, Jiajun Li, Han Zhang, Yilin Zhao, Zaid Pervaiz Bhat, Shuyu Yang, Ashutosh Kumar, Rong Wang, Rafael Martin Nieto, Peter Christiansen, Ahmed Abduljawad, Mohanrasu Shanmugam, Nadeem Shaik, Sujit Biswas, Xunlei Wu, Vidya Murali, Rama Chellappa