Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Drew G Bresnahan,Yang Li

doi:10.1109/access.2021.3096465

Abstract

Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique time-frequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.

Highlights

Driver drowsiness and distraction is a significant cause of automobile traffic accidents
The goal of this study is to measure and classify eight common head movement patterns exhibited by motor vehicle drivers using a millimeter-wave FMCW radar in a moving car interior environment with passengers sitting nearby
DATA PROCESSING The radar data is post-processed using a customized MATLAB script: a two-dimensional fast-Fourier transform (FFT) is taken along each frame, resolving the range-Doppler profile of the car cabin at each time sample

Summary

Introduction

Driver drowsiness and distraction is a significant cause of automobile traffic accidents. Analyzing the motion of the driver’s head allows one to more accurately infer the driver’s drowsiness condition [3],[4]. The two most popular noncontact approaches are camera-based video processing and radar sensing. Et al [6] laid foundational work in developing a video-fed algorithm to monitor driver head and eye motion. Tawari et al [7] used a multiple camera vision algorithm to monitor and detect driver behavior in a variety of occlusion and lighting conditions. Additional studies have been conducted to develop models to analyze head motion using video capture methods, as seen in [8]-[10]. Poor lighting conditions and headworn accessories like sunglasses limit the usefulness of video processing in more general scenarios

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