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. 2023 Aug 30;23(17):7536.
doi: 10.3390/s23177536.

Identification of Driver Status Hazard Level and the System

Jiayuan Gong  1   2   3 , Shiwei Zhou  2   3 , Wenbo Ren  2   3
Affiliations

Identification of Driver Status Hazard Level and the System

Jiayuan Gong et al. Sensors (Basel). .

Abstract

According to the survey statistics, most traffic accidents are caused by the driver's behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver's state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver's current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver's dangerous driving behavior by detecting unsafe objects in the cab and the driver's posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

Keywords: Dlib; Image Identification; YOLOX; driver danger levels.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
YOLOX network structure.
Figure 2
Figure 2
Target detection system framework.
Figure 3
Figure 3
Data annotation.
Figure 4
Figure 4
YOLOX training curve.
Figure 5
Figure 5
(a) Cigarette test results; (b) phone test results.
Figure 6
Figure 6
Detection efficiency of different models.
Figure 7
Figure 7
Joint point connection.
Figure 8
Figure 8
OpenPose schematic.
Figure 9
Figure 9
(a) Hand coordinates; (b) ear coordinates.
Figure 10
Figure 10
Corresponding flow chart of phone call status.
Figure 11
Figure 11
(a) Depthwise convolutional filters; (b) pointwise convolution filters; (c) depthwise separable convolution.
Figure 12
Figure 12
Face detection.
Figure 13
Figure 13
(a) Pre-optimization detection rate and (b) optimized detection rate.
Figure 14
Figure 14
Keypoint detection.
Figure 15
Figure 15
Eye feature points.
Figure 16
Figure 16
Eyes open.
Figure 17
Figure 17
Driver status.
Figure 18
Figure 18
OpenMV Connection Diagram.
Figure 19
Figure 19
(a) Dynamic tracking of the detection process 1 (b) Dynamic tracking of the detection process 2.
See this image and copyright information in PMC

References

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    1. Traffic Administration of the Ministry of Public Security . Annual Report of Road Traffic Accident Statistics of the People’s Republic of China (2020) Traffic Administration of the Ministry of Public Security; Beijing, China: 2021.
    1. Zhang L., Liu T., Pan F., Guo T., Liu R. Analysis of the Influence of Driver Factors on Road Traffic Accident Indicators. China J. Saf. Sci. 2014;24:79–84.
    1. Rau P. Drowsy driver detection and warning system for commercial vehicle drivers: Field operational test design, analysis, and progress; Proceedings of the 19th International Conference on Enhanced Safety of Vehicles; Washington, DC, USA. 6–9 June 2005.
    1. Ding Y. Research on the Characteristics of Drivers in Urban Traffic Accidents Based on Data Mining. Shenyang University; Shenyang, China: 2018.

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