# Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network

arXiv: https://arxiv.org/abs/2407.20633 · HTML: https://mlsanity.com/paper/2407.20633/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers](https://mlsanity.com/paper/2207.12148/) (2022)

## Built on by (most cited first, 4 of 4)

- [Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques](https://mlsanity.com/paper/2505.01973/) (2025)
- [A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection](https://mlsanity.com/paper/2501.11758/) (2025)
- [SynSacc: A Blender-to-V2E Pipeline for Synthetic Neuromorphic Eye-Movement Data and Sim-to-Real Spiking Model Training](https://mlsanity.com/paper/2602.08726/) (2026)
- [ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification](https://mlsanity.com/paper/2608.27150/) (2026)

## Abstract (arXiv)

Event camera-based driver monitoring is emerging as a pivotal area of research, driven by its significant advantages such as rapid response, low latency, power efficiency, enhanced privacy, and prevention of undersampling. Effective detection of driver distraction is crucial in driver monitoring systems to enhance road safety and reduce accident rates. The integration of an optimized sensor such as Event Camera with an optimized network is essential for maximizing these benefits. This paper introduces the innovative concept of sensing without seeing to detect driver distraction, leveraging computationally efficient spiking neural networks (SNN). To the best of our knowledge, this study is the first to utilize event camera data with spiking neural networks for driver distraction. The proposed Spiking-DD network not only achieve state of the art performance but also exhibit fewer parameters and provides greater accuracy than current event-based methodologies.