Event-Driven Neuromorphic Architecture for Energy-Efficient Driver Distraction Detection
DOI:
https://doi.org/10.15837/ijccc.2026.4.7221Keywords:
neuromorphic computing, driver distraction detection, energy efficiency, embedded systems, spiking neural networks, dynamic vision sensor, event-driven processing, ultra-low powerAbstract
This study proposes an energy-efficient framework for embedded driver distraction detection based on brain-inspired computing. The framework integrates a dynamic vision sensor (DVS) with a spiking neural network (SNN) and incorporates dissipative wave dynamics to achieve efficient feature propagation. The experimental results demonstrated that the system maintained high detection accuracy and energy efficiency across various driving scenarios. The proposed system achieved over 99% accuracy in complex scenarios while reducing power consumption by more than 85% compared to the GPU-CNN baseline. These improvements demonstrate the feasibility of event-driven neuromorphic computing for sustainable embedded driver monitoring. The research provides new insights for the practical deployment of low-power automotive safety systems.
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