Integrated Sensing and Edge Fusion for Consumer Vehicle Road Anomaly Monitoring

Author First name, Last name, Institution

• Abuzar Khan, CECOS University of IT & Emerging Sciences
• Ahmad Junaid, CECOS University of IT & Emerging Sciences
• Abid Iqbal, King Faisal University
• Anwer Al-Dulaimi, Zayed University, Abu Dhabi CampusFollow
• Ghassan Husnain, CECOS University of IT & Emerging Sciences
• Mohammad Alhefdi, King Khalid University

Document Type

Article

Source of Publication

IEEE Transactions on Consumer Electronics

Publication Date

1-1-2026

Abstract

Consumer vehicles and aftermarket devices embed cameras, inertial sensors and GNSS, enabling road-condition awareness as a consumer electronics service. Yet manual surveys and isolated sensing remain costly and inconsistent at city scale, while road traffic injuries impose a socio-economic burden often estimated at about 3% of GDP. Single-source approaches fail under field conditions because camera monitoring degrades in low illumination and occlusion, inertial sensing is sensitive to vehicle dynamics and speed variation and satellite positioning can be sparse or unstable for precise defect localisation. This paper proposes a unified vehicle-mounted sensing and edge computing pipeline that integrates visual, inertial and positioning streams and couples reliability-aware alignment and fusion with communication-efficient reporting for decision making. The system learns geo temporal representations through self-supervised pretraining, synchronises asynchronous streams online using reliability-weighted Dynamic Time Warping and performs hierarchical transformer fusion with reliability gating to control cross-sensor dependencies under time-varying quality. Window-level predictions are converted into compact georeferenced events that can be transmitted over bandwidth-limited links and aggregated into Geographic Information System ready heatmaps and priority segments. On the Passive Vehicular Sensors dataset, the proposed approach achieves 0.91 accuracy and 0.90 macro averaged F1, improving over early fusion at 0.84 accuracy. The results indicate that reliability-aware multimodal learning improves robustness and enables consumer-grade road monitoring with actionable geospatial outputs.

DOI Link

10.1109/tce.2026.3697336

ISSN

0098-3063

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Disciplines

Computer Sciences

Keywords

Consumer vehicles, Edge computing, GIS-ready mapping, Multimodal sensing, Reliability-aware fusion

Scopus ID

105040206364

Recommended Citation

Khan, Abuzar; Junaid, Ahmad; Iqbal, Abid; Al-Dulaimi, Anwer; Husnain, Ghassan; and Alhefdi, Mohammad, "Integrated Sensing and Edge Fusion for Consumer Vehicle Road Anomaly Monitoring" (2026). All Works. 8224.
https://zuscholars.zu.ac.ae/works/8224

Indexed in Scopus

yes

Open Access

no