Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection

Abstract

Driver Monitoring Systems (DMS) represent a promising approach for enhancing driver safety within vehicular technologies. This research explores the integration of neuromorphic event camera technology into DMS, offering faster and more localized detection of changes due to motion or lighting in an imaged scene. When applied to the observation of a human subject event camera provides a new level of sensing capabilities over conventional imaging systems. The study focuses on the application of DMS by incorporating the event cameras, augmented by submanifold sparse neural network models (SSNN) to reduce computational complexity. To validate the effectiveness of proposed machine learning pipeline built on event data we have opted the Driver Distraction as a critical use case. The SSNN model is trained on synthetic event data generated from the publicly available Drive&Act and Driver Monitoring Dataset (DMD) using a video-to-event conversion algorithm (V2E). The proposed approach yields comparable performance with state-of-the-art approaches, achieving an accuracy of 86.25% on the Drive&Act dataset and 80% on comprehensive DMD dataset while significantly reducing computational complexity. In addition, to demonstrate the generalization of our results the network is also evaluated using locally acquired event dataset gathered from a commercially available neuromorphic event sensor.