TY - JOUR
T1 - Inattentive Driving Behavior Detection Based on Portable FMCW Radar
AU - DIng, Chuanwei
AU - Chae, Rachel
AU - Wang, Jing
AU - Zhang, Li
AU - Hong, Hong
AU - Zhu, Xiaohua
AU - Li, Changzhi
N1 - Funding Information:
Manuscript received March 11, 2019; revised June 14, 2019; accepted July 31, 2019. Date of publication August 27, 2019; date of current version October 4, 2019. This work was supported in part by the National Science Foundation (NSF) under Grant ECCS-1808613 and Grant CNS-1718483, in part by the National Natural Science Foundation of China under Grant 61871224 and Grant 81601568, in part by the Key Research and Development Plan of Jiangsu Province under Grant BE2018729, in part by the Fundamental Research Funds for the Central Universities under Grant 30917011316, and in part by the State Scholarship Fund of China Scholarship Council under Grant 201806840055. This article is an expanded version from the IEEE MTT-S Radio and Wireless Week (Wireless Sensors and Sensor Networks), Orlando, FL, USA, January 20–23, 2019. (Corresponding author: Hong Hong.) C. Ding is with the School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China, and also with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409 USA.
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Monitoring driver's attentiveness is crucial for transportation safety. In this article, inattentive driving behavior detection based on frequency-modulated continuous-wave (FMCW) radar systems is proposed for this purpose. Seven typical driving behaviors which result in reduced attentiveness are involved in this study. Time-Doppler spectrogram and range-Doppler trajectory are utilized to analyze their features in multiple domains, including time, Doppler, range, and radar cross-section (RCS). These features are extracted as inputs to a machine learning classifier to obtain recognition results. Extensive experiments on a real car environment have been conducted to show its feasibility and superiority by obtaining an average accuracy rate of around 95%. The influences of radar center frequency, individual diversity, and radar view angle are also investigated.
AB - Monitoring driver's attentiveness is crucial for transportation safety. In this article, inattentive driving behavior detection based on frequency-modulated continuous-wave (FMCW) radar systems is proposed for this purpose. Seven typical driving behaviors which result in reduced attentiveness are involved in this study. Time-Doppler spectrogram and range-Doppler trajectory are utilized to analyze their features in multiple domains, including time, Doppler, range, and radar cross-section (RCS). These features are extracted as inputs to a machine learning classifier to obtain recognition results. Extensive experiments on a real car environment have been conducted to show its feasibility and superiority by obtaining an average accuracy rate of around 95%. The influences of radar center frequency, individual diversity, and radar view angle are also investigated.
KW - Driving behavior monitoring
KW - frequency-modulated continuous-wave (FMCW) radar
KW - machine learning
KW - range-Doppler
KW - time-Doppler
UR - http://www.scopus.com/inward/record.url?scp=85077438314&partnerID=8YFLogxK
U2 - 10.1109/TMTT.2019.2934413
DO - 10.1109/TMTT.2019.2934413
M3 - Article
AN - SCOPUS:85077438314
VL - 67
SP - 4031
EP - 4041
JO - IEEE Transactions on Microwave Theory and Techniques
JF - IEEE Transactions on Microwave Theory and Techniques
SN - 0018-9480
IS - 10
M1 - 8815861
ER -