One‐shot learning‐based driver's head movement identification using a millimetre‐wave radar sensor. Issue 5 (7th January 2022)

• Record Type: Journal Article
• Authors: Nguyen, Hong Nhung; Lee, Seongwook; Nguyen, Tien‐Tung; Kim, Yong‐Hwa
• Is Part Of: IET radar, sonar & navigation. Volume 16:Issue 5(2022)
• Page Start: 825
• Page End: 836
• Publication Date: 2022-01-07
• Subjects: convolutional neural nets -- driver information systems -- learning (artificial intelligence) -- pattern classification -- radar signal processing; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12223
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him/her for avoiding incidents related to traffic accidents. In this paper, to meet the requirement, based on radar sensors applications, the authors first use a small‐sized millimetre‐wave radar installed at the steering wheel of the vehicle to collect signals from different head movements of the driver. The received signals consist of the reflection patterns that change in response to the head movements of the driver. Then, in order to distinguish these different movements, a classifier based on the measured signal of the radar sensor is designed. However, since the collected data set is not large, in this paper, the authors propose One‐shot learning to classify four cases of driver's head movements. The experimental results indicate that the proposed method can classify the four types of cases according to the various head movements of the driver with a high accuracy reaching up to 100%. In addition, the classification performance of the proposed method is significantly better than that of the convolutional neural network (CNN) model.