DRIVERDAPP: Driver’s distraction record using deep learning and blockchain

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. Red–green–blue (RGB) in-cabin image frames captured by a dashboard camera are processed locally on an NVIDIA Jetson Nano edge device, where a fine-tuned You Only Look Once version 11 small (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.