Existing driver distraction detection systems face critical barriers to real-world deployment in safety-critical transportation environments, including the lack of real-time edge inference, explainable artificial intelligence (XAI), trustworthy event logging, and privacy-preserving evidence management. To overcome these challenges, this paper presents an integrated framework, termed DRIVERDAPP, that unifies real-time edge-based detection, AI explainability, and secure, auditable event management. RGB in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned YOLOv11s model classifies ten driver behavior states and triggers in-vehicle audio alerts for unsafe activities. To suppress transient misclassifications under edge constraints, distraction persistence is verified using a lightweight temporal confirmation strategy. Confirmed distraction events are immutably recorded via Solidity-based smart contracts and submitted through the Web3.py interface to a permissioned Hyperledger Besu consortium blockchain operating under Quorum Byzantine Fault Tolerance (QBFT) consensus. Privacy is preserved by retaining raw visual data off-chain, while only pseudo-anonymous identifiers and event metadata are stored on-chain under controlled access policies. Model interpretability is enabled using Gradient-weighted Class Activation Mapping (Grad-CAM), providing transparent visual explanations of distraction-related predictions. The framework is evaluated using the State Farm Distracted Driver and American University in Cairo datasets, demonstrating stable real-time edge operation, negligible blockchain query latency, and secure smart contract execution. These results confirm the suitability of DRIVERDAPP for secure, explainable, and deployable driver monitoring in intelligent transportation systems.
| Repository | Purpose |
|---|---|
| driverdapp_ai | Driver distraction detection, Grad-CAM explainability |
| driverdapp_contract | Solidity smart contracts |
| driverdapp_netbench | Hyperledger Besu permissioned network configuration and benchmarking |
- State Farm Distracted Driver Detection — Kaggle competition dataset (10 classes,
c0–c9): https://www.kaggle.com/c/state-farm-distracted-driver-detection - American University in Cairo (AUC) Distracted Driver dataset (Distracted Driver V2): https://heshameraqi.github.io/distraction_detection
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Odinachi Udemezuo Nwankwo
— Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
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Simeon Okechukwu Ajakwe
— Smart Computing Department, Kyungdong University Global Campus, Goseong-gun, South Korea
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Muhammad Rasyid Redha Ansori
— NSLab Inc., Gumi, South Korea
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Gifar Arif Haryadi
— Department of IT Convergence Engineering & ICT Convergence Research Center, Kumoh National Institute of Technology, Gumi, South Korea
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Dong-Seong Kim
— Department of IT Convergence Engineering & ICT Convergence Research Center, Kumoh National Institute of Technology, Gumi, South Korea; NSLab Inc., Gumi, South Korea
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Jae Min Lee ✉
— Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea (Corresponding author)
If you use this work in your research, please cite the DRIVERDAPP paper:
@article{Nwankwo2026,
title = {DRIVERDAPP: Driver’s distraction record using deep learning and blockchain},
author = {Nwankwo, Odinachi Udemezuo and Ajakwe, Simeon Okechukwu and
Ansori, Muhammad Rasyid Redha and Haryadi, Gifar Arif and
Kim, Dong-Seong and Lee, Jae Min},
journal = {Computers and Electrical Engineering},
volume = {139},
pages = {111340},
year = {2026},
issn = {0045-7906},
doi = {10.1016/j.compeleceng.2026.111340}
}This repository is licensed under the MIT License. Copyright © 2026 The DRIVERDAPP Authors.