Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
Abstract
Continuous traffic monitoring is critical for accurate structural load assessment and fatigue life estimation of highway bridges. However, conventional vision-based methods suffer from limitations such as line-of-sight restrictions, susceptibility to adverse weather and lighting conditions, and limited spatial coverage. Distributed acoustic sensing (DAS) offers a robust alternative by repurposing existing telecommunications dark fiber into dense, kilometer-scale sensor arrays. Nevertheless, interpreting the complex DAS signals generated by vehicular traffic remains challenging due to overlapping dynamic signatures and the scarcity of ground-truth data for model training. To overcome this, we present a cross-modal (vision-to-optic) supervision framework that transforms existing fiber infrastructure into a traffic monitoring system. We deployed a synchronized camera–DAS testbed along a roadway segment served by dark fiber. Video data is processed using modern computer vision foundation models SAM3 to automatically extract vehicle trajectories and classifications. These camera-derived labels supervise a deep sequence learning model trained on the corresponding DAS strain data. Once trained, the fiber-optic system independently achieves accurate vehicle detection, classification, localization, and speed estimation. Finally, we demonstrate how these continuous, DAS-derived traffic metrics can be directly translated into dynamic load profiles, providing a scalable, continuous monitoring solution for bridge fatigue and structural health assessment.
Abstract. Pavement Management Systems (PMS) are essential for evaluating and maintaining transportation infrastructure; however, conventional monitoring methods are often labour-intensive, costly, and inaccurate. The growing need for reliable. timely pavement condition data has driven the development of automated, data-driven approaches. This study presents a low-cost and scalable framework for pavement condition monitoring that integrates multimodal sensing with a digital twin (DT) environment. Smartphones equipped with inertial measurement unit (IMU) sensors, GPS, and cameras are used to collect synchronized vibration and visual data during normal driving conditions. Vibration signals are analysed to detect anomalies associated with pavement surface irregularities, while video data are processed using a deep learning-based object detection model to identify surface distress. A late fusion approach combines the outputs from both modalities to improve detection reliability and provide comprehensive condition assessment. The system enables spatial mapping of detected distresses and supports real-time visualization through a web-based DT dashboard. Results demonstrate that multimodal sensing compensates for the limitations of individual sensors, enhancing both detection accuracy and robustness. The proposed framework offers a practical solution for efficient pavement monitoring. It supports data-driven decision-making for proactive infrastructure management, with potential for future expansion through crowdsourced data and additional sensing technologies.
Deepak Satheesan, Songnian Li, Michael A. Chapman· The International Archives o...· 0 citations
This paper introduces distributed acoustic sensing (DAS) as an emerging sensing technique for large-scale traffic state perception (TSP) on expressways. By enabling optical fibers to operate as dense sensing arrays, DAS offers a promising solution for continuous traffic monitoring along expressway corridors. However, the raw traffic information extracted from DAS is inaccurate, which limits its direct application to large-scale TSP. This study proposes a physics-informed neural network (PINN) framework for DAS-based TSP. A physics-based DAS simulation platform is developed and validated against field observations which provides a flexible testbed for generating data under diverse traffic scenarios. On this basis, two network architectures, ResUNet and Fourier Neural Operator, are investigated in combination with three types of traffic flow constraints, namely the Lighthill-Whitham-Richards (LWR) model, LWR with a fundamental diagram, and the Aw-Rascle-Zhang model. Totally, 16 PINN models are constructed and evaluated. The results show that the proposed PINN framework effectively improves the accuracy of DAS-based TSP compared to the solely data-driven baselines. Among the physical constraints considered, the LWR-based models show the best overall performance. In addition, few-shot transfer learning can enhance model performance in previously unseen scenarios, demonstrating the potential of the proposed framework for adaptation to site-specific deployment conditions.
Yang Ma, Dianwei Zhou, Yang Liu et al.· Communications in Transporta...· 0 citations
Highway pavement networks require frequent condition assessment, but conventional inspections with dedicated survey vehicles remain costly and are conducted at long intervals. This delays timely maintenance and increases life-cycle costs. This study presents an AI-based pavement condition assessment framework based on multi-modal sensor fusion, integrating video and vibration data from a vehicle-mounted smartphone with distributed fiber-optic sensing (FOS) installed along the roadside. These two data collection approaches are complementary, as the smartphone sensors cover the full network at low cost but produce noisier data, whereas FOS offers high-fidelity measurements only at equipped sections. Data from all three modalities were collected over multiple runs on a Korean expressway and evaluated across seven scenarios covering both individual and fused sensor configurations. Three tree-based classifiers (LightGBM, XGBoost, Random Forest) were trained to predict the International Roughness Index (IRI), a roughness-based index derived from the road's longitudinal profile, and the Highway Pavement Condition Index (HPCI), a composite index that incorporates both roughness and surface distress. The results indicate that vibration features contributed effectively to both IRI and HPCI by reflecting the vehicle's response to surface irregularities. Vision features contributed primarily to HPCI by capturing surface defects, while FOS features improved both predictions by providing structural response measurements inaccessible to the vehicle-mounted sensors. The findings demonstrate that combining modalities generally outperformed individual modalities, but the most effective combination differed by target index, offering practical guidance on how to pair sensing modalities for different pavement condition metrics.
Jinwoo Lee, B. Kim, Y. An· e-Journal of Nondestructive...· 0 citations
Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety depend on perceiving signs correctly across regions, ranges, and weather conditions. Despite recent progress, vision-based methods continue to face three fundamental limitations: poor cross-regional generalization due to high diversity across countries, degraded performance on small-object detection at long ranges (traffic signs occupy as little as $10{\times}10$ pixels at 200m), and fragile temporal tracking under the strongly non-linear perspective distortion that occurs as a vehicle approaches a sign. In this paper, we address the problem of robust, long-range, region-agnostic traffic sign perception by combining camera and Light Detection and Ranging (LiDAR) sensing. We present a multi-modal detection framework whose Intensity-Aware Deformable Fusion module aligns retro-reflective LiDAR cues with camera features, anchoring detection on geometric invariants rather than region-specific visual appearance. We further introduce a dual motion-model tracker that explicitly accounts for non-linear perspective transformations during vehicle approach, substantially improving temporal consistency over linear motion assumptions. Additionally, we develop a semantic attribute classification pipeline that estimates occlusion level, readability, sign embeddedness, and road relevance, providing actionable context to downstream planning. Extensive evaluation on our dataset, spanning 60+ countries and 2,500+ hours of driving data, shows that the proposed pipeline achieves an Object Miss Ratio (OMR) of 0.49% across 221,068 evaluation sequences, demonstrating globally generalizable traffic sign perception in commercial-grade autonomous driving systems.
Meda Lazar, S. Sridhar, Shashwata Gupta et al.· 0 citations
Urban noise pollution is a critical public health problem affecting millions of people around the world. Current acoustic monitoring systems largely focus on optimizing classification accuracy but fail to meet operational requirements such as calibrated probability outputs, deployment formats, and spatiotemporal integration. This study presents a comprehensive urban noise monitoring pipeline extending from raw sensor data to city-scale policy support. The proposed framework uses a CNN14-based ensemble architecture on the SONYC-UST dataset collected from 56 acoustic sensors deployed across New York City. The system performs simultaneous multi-source detection across 35 hierarchical acoustic presence labels and provides reliable probability calibration validated by a Brier score of 0.1269. In terms of technical infrastructure, the pipeline offers export in TorchScript format that can run on edge devices without Python dependency, processing capacity of more than 2,000 audio segments per second on an NVIDIA GeForce RTX 3060 and training time of less than six hours per fold. Integrated 250-meter spatial grid mapping and hourly-daily temporal aggregation modules enable direct integration of predictions into urban planning workflows. Independent external corroboration was performed using NYC 311 complaint records across 3,460 matched grid-day observations. Permutation testing revealed a statistically significant relationship between predicted noise scores and community-reported disturbance. This study demonstrates that model accuracy alone is not sufficient for deployable urban noise monitoring systems, and that a holistic approach encompassing calibration, deployment, and spatial integration is required.
Mehmet Ali Yalçınkaya· Scientific Reports· 0 citations
Intelligent Transportation Systems deployed on highways predominantly rely on conventional RGB cameras for traffic perception and vehicle tracking. However, highway environments present unique challenges: the absence of artificial lighting infrastructure, combined with high vehicle velocities, results in severely degraded perception performance under low-light conditions. Specifically, nighttime scenarios suffer from motion blur, insufficient exposure, and poor signal-to-noise ratios, which catastrophically impair the reliability of RGB-based sensing systems. To address these limitations, we propose a novel Joint Event-RGB Adaptive Tracking (JEAT) framework. Unlike existing multi-sensor trackers constrained by rigid, hard-coded prioritization, JEAT merges asynchronous event streams and RGB frames into a unified joint data association optimization. By employing an Adaptive Extended Kalman Filter to continuously estimate measurement noise via NIS statistics, the framework dynamically weights and fuses both modalities, optimally harnessing event streams during dark or high-speed motion while leveraging RGB frames under bright or static conditions. Furthermore, given the absence of publicly available datasets tailored for event-based highway perception with diverse environmental conditions, we present SEHN, a large-scale synthetic dataset generated using the CARLA simulator. Our dataset encompasses diverse environmental conditions (daytime, nighttime, nighttime with out artificial lighting) and varying traffic densities, providing synchronized RGB imagery and event streams to facilitate multi-modal fusion research. Our code and datasets will be available at https://github.com/haidongwang96/SEHN.
Haidong Wang, Hengxing Cai, Wanlei Li et al.· 0 citations
