Pavement

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 17

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Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?
Sep 15, 2026Seung Jae Lieu, Diego Morra, Chiara Cadoni +3Pedestrian Detection Accessibility
Senseable City Lab Massachusetts Institute of Technology Cambridge, MA 02139
RMR-Net: Degradation-Evidence-Guided Road-Image Restoration for Defect Detection
Aug 9, 2026Amir Ghorbani, Amirali K. Gostar, WeiQin Chuah +3Pavement Image Restoration
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
Aug 2, 2026Jianglei Xing, Xiao Tan, Dongzhao Jin +3Shapley Additive Explanations Pavement
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, 210024, China · State Key Laboratory of Water Disaster Prevention, Nanjing, Jiangsu, 210024, China · Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, Michigan, 49931, United States +3
RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
Jul 16, 2026Tianchen Deng, Zhiheng Feng, Wenhua Wu +3Pavement Surface Reconstruction
School of Automation and Intelligent Sensing, Shanghai Jiao Tong university and State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis, Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai 200240, China
3D Lane Detection with Odometry for High-Speed Vehicle Racing
Jul 15, 2026Omoruyi Atekha, John Subosits, Marcus GreiffAutonomous Racing 3D Object Detection
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. · Toyota Research Institute (TRI), 94022 Los Altos, CA, USA.
A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions
Jun 17, 2026Yamil Uchani, Grace Luna, Edwin Salcedo +1Unmanned Aerial Vehicle Detection Pavement
Department of Mechatronics Engineering, Universidad Cat´olica Boliviana “San Pablo”, Bolivia · School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom
DNN Koopman-Based Deviation Compensation for UGV Path Tracking Control on Coupled Slope and Potholed Road
Jun 17, 2026Jian Zhao, Wenbo Zhou, Zhicheng Chen +5Pavement Vehicle Dynamics
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130000, China · Xiamen King Long United Automotive Industry Co., Ltd., Xiamen 361023, China
Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
Jun 2, 2026Yongjin Choi, Hyeonbin Moon, Seunghwa RyuParametric Physics-Informed Neural Network Finite Element Method
KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising
Jun 1, 2026Vishal Hariharan, Salar Basiri, Kanwar Bharat SinghPavement Vehicle Dynamics
Global Tire Intelligence and Solutions The Goodyear Tire and Rubber Company Akron, Ohio, USA · Department of Mechanical Engg. University of Illinois Urbana Champaign (UIUC), USA
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
May 28, 2026Marouane Elmegdar, Teng XiaoSurface Reconstruction Pavement
School of Computer Science, Hubei University of Technology, Wuhan, China 430068
Pixel-Level Pavement Distress Assessment Using Instance Segmentation
May 25, 2026Logan Dewick, Bibesh Pyakurel, Kong Pheng Yang +2Pavement
University of Wisconsin - Green Bay · Computer Science Department University of Wisconsin–Green Bay, Green Bay, WI, USA
From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
May 19, 2026Ahmed Y. Gado, Omar Y. Goba, Alaa Hassanein +2Temporal Grounding Agentic Reasoning
Computer Science & Engineering Department, German University in Cairo (GUC), Egypt · C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt
CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography
May 6, 2026Gasser Elazab, Frank Neuhaus, Tilman Koß +5Naturalistic Driving Data Pavement
1CARIAD SE · 2Technische Universität Berlin · 3Vision & Robotics GmbH
Deep learning-based pavement performance modeling using multiple distress indicators and road work history
May 3, 2026Lu Gao, Zhe Han, Yunshen ChenPavement Distress
Department of Construction Management, University of Houston · Center for Transportation Research, The University of Texas at Austin · Independent Scholar
Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
Apr 27, 2026Shaunak Kolhe, Peng Jiang, Maggie Wigness +4Odometry Radar
Texas A&M University · DEVCOM Army Research Laboratory
Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation
Apr 27, 2026Haosong Xiao, Yamini Ramesh, Rishabh Shukla +2Pavement
Department of Mechanical and Aerospace Engineering · University at Buffalo (SUNY), Buffalo, New York, USA · School of Engineering and Applied Science
Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
Feb 18, 2026Daniil Lisus, Katya M. Papais, Cedric Le Gentil +4Naturalistic Driving Data Lidar
University of Toronto Institute of Aerospace Studies, Toronto, Canada · Trimble, Canada

Sep 15, 2026, cs.CVJ/K move · Enter open · S save

Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?

Seung Jae Lieu, Diego Morra, Chiara Cadoni, Wonseop Song+2

Senseable City Lab Massachusetts Institute of Technology Cambridge, MA 02139

Pedestrian Detection Accessibility Pavement Urban Environments Ground Truth

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An important component of urban accessibility, particularly for wheelchair users and people with reduced mobility, is sidewalk compliance with measurable requirements. We test whether effective width, longitudinal slope, cross slope, and pavement condition can be assessed reliably from pedestrian-level imagery using vision-language models (VLMs). We present the first application of sampling-based conformal prediction (CP) for VLM-based accessibility assessment. We evaluate four VLMs on 514 sidewalk images from Seoul, South Korea, with field-measured ground truth. Conformal calibration attains the nominal 90% coverage for all models and attributes, but the calibrated regions differ in informativeness. Effective width yields the most informative estimates, with a mean interval half-width of about 1.0 m for the best model. Since every model overestimates width, asymmetric calibration shortens the intervals by up to 33% at unchanged coverage. Longitudinal slope is marginally informative, cross-slope intervals are too wide to resolve regulatory thresholds, and pavement-condition sets degenerate to all five grades (A-E) for three of the four models. Uncalibrated intervals from raw sampling dispersion cover only 17-47% of field-measured values at a nominal 90% level. Among the images with the most self-consistent responses, these intervals miss the field-measured value in up to 96% of cases. Response self-consistency is therefore not evidence of accuracy, and sampling dispersion cannot be interpreted as uncertainty until it has been calibrated against field-measured ground truth. No quantitative attribute reaches the precision required for general compliance assessment, but CP identifies from calibration data alone which attributes can support screening of segments far from the thresholds. We release the annotated pedestrian-level images and their corresponding field-measured attribute values.

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