Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
Source abstract
Abstract
Detecting the factors affecting drivers’ safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sleep and Work-Related Fatigue · Traffic and Road Safety · Human-Automation Interaction and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Department of Standard and Quota, Ministry of Housing and Urban-Rural Development, Beijing 100835, China
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Funding
China Postdoctoral Science Foundation Project · Basic Scientific Research Business Projects of Beijing Municipal Universities (Youth Scientific Research and Innovation) · Ministry of Housing and Urban Rural Development Science and Technology Project Plan
References (81)
Show source-provided references
1. National Bureau of Statistics of China (2024, March 01). Statistical Communique of the People’s Republic of China on the 2022 National Economic and Social Development, Available online: https://www.stats.gov.cn/english/PressRelease/202302/t20230227_1918979.html.
2. A review of traffic accidents and related practices worldwide10.2174/1874447801913010065 ↗
3. Road traffic injuries: A major global public health crisis10.1016/j.puhe.2008.06.009 ↗
4. Human factors related to major road traffic accidents in China10.1080/15389588.2019.1670817 ↗
5. Road traffic accidental injuries and deaths: A neglected global health issue10.1002/hsr2.1240 ↗
6. A review of the influencing factors and intervention methods of drivers’ hazard perception ability
7. Khan, M.Q., and Lee, S. (2019). A comprehensive survey of driving monitoring and assistance systems. Sensors, 19.10.3390/s19112574 ↗
8. Factors affecting the number of road traffic accidents in Kerman province, southeastern Iran (2015–2021)10.1038/s41598-023-33571-8 ↗
9. Frequency, determinants, and consequences of different drivers’ emotions: An on-the-road study using self-reports,(observed) behaviour, and physiology10.1016/j.trf.2007.05.001 ↗
10. Hassib, M., Braun, M., Pfleging, B., and Alt, F. (2019, January 2–6). Detecting and influencing driver emotions using psycho-physiological sensors and ambient light. Proceedings of the IFIP Conference on Human-Computer Interaction, Paphos, Cyprus.10.1007/978-3-030-29381-9_43 ↗
11. Research hotspots and evolution analysis of domestic traffic psychology
12. Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers
13. Computer vision-based recognition of driver distraction: A review10.1002/cpe.6475 ↗
14. Deep learning and autonomous vehicles: Strategic themes, applications, and research agenda using SciMAT and content-centric analysis, a systematic review10.3390/make5030041 ↗
15. Review of machine and deep learning techniques in epileptic seizure detection using physiological signals and sentiment analysis10.1145/3552512 ↗
16. Research status and prospect of human-machine collaborative control of intelligent vehicles
17. Hatoyama, K., Nishioka, M., Kitajima, M., Nakahira, K., and Sano, K. (2019, January 9–12). Perception of time in traffic congestion and drivers’ stress. Proceedings of the International Conference on Transportation and Development 2019, Alexandria, VA, USA.10.1061/9780784482575.017 ↗
18. The role of driver distraction in crashes: An analysis of 1995–1999 Crashworthiness Data System Data
19. Structural analysis of driver fatigue behavior: A systematic review
20. The relationship between fatigue and driving performance: A review and directions for future research
21. A survey on vision-based driver distraction analysis10.1016/j.sysarc.2021.102319 ↗
22. Goodsell, R., Cunningham, M., and Chevalier, A. (2019). Driver Distraction: A Review of Scientific Literature, ARRB Report Project.
23. Risk of motor vehicle accidents related to sleepiness at the wheel: A systematic review and meta-analysis10.1093/sleep/zsx134 ↗
24. Distracted driving and risk of road crashes among novice and experienced drivers10.1056/NEJMsa1204142 ↗
25. The effect of cognitive load on Detection-Response Task (DRT) performance during day-and night-time driving: A driving simulator study with young and older drivers10.1016/j.trf.2023.07.002 ↗
26. Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance10.1016/j.trf.2023.04.013 ↗
27. Fatigue, sleepiness, and performance in simulated versus real driving conditions10.1093/sleep/28.12.1511 ↗
28. A validation study of a fixed-based, medium fidelity driving simulator for human–machine interfaces visual distraction testing10.1049/itr2.12362 ↗
29. Chen, H., Zhe, M., and Hui, Z. (2022, January 4). The influence of driver fatigue on takeover reaction time in human-machine co-driving environment. Proceedings of the World Transport Congress, Beijing, China.
30. Yongfeng, M., Xin, T., and Shuyan, C. (2022, January 8–12). The impact of distracted driving behavior on ride-hailing vehicle control under natural driving conditions. Proceedings of the 17th China Intelligent Transportation Annual Conference, Macau, China.
31. Ngxande, M., Tapamo, J.-R., and Burke, M. (December, January 30). Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques. Proceedings of the 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), Bloemfontein, South Africa.10.1109/RoboMech.2017.8261140 ↗
32. Dinges, D.F., and Grace, R. (1998). PERCLOS: A Valid Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance, Tech. Rep. MCRT-98-006.
33. Driver fatigue detection based on improved Yolov3
34. Research on key technologies of intelligent transportation based on image recognition and anti-fatigue driving10.1186/s13640-018-0403-6 ↗
35. Driver yawning detection based on subtle facial action recognition10.1109/TMM.2020.2985536 ↗
36. A fatigue driving detection algorithm based on facial multi-feature fusion10.1109/ACCESS.2020.2998363 ↗
37. A real-time driving drowsiness detection algorithm with individual differences consideration10.1109/ACCESS.2019.2958667 ↗
38. Zheng, H., Wang, Y., and Liu, X. (2023). Adaptive Driver Face Feature Fatigue Detection Algorithm Research. Appl. Sci., 13.10.3390/app13085074 ↗
39. Liu, W., Qian, J., Yao, Z., Jiao, X., and Pan, J. (2019). Convolutional two-stream network using multi-facial feature fusion for driver fatigue detection. Future Internet, 11.10.3390/fi11050115 ↗
40. Intelligent driver drowsiness detection for traffic safety based on multi CNN deep model and facial subsampling10.1109/TITS.2021.3134222 ↗
41. Kim, W., Jung, W.-S., and Choi, H.K. (2019). Lightweight driver monitoring system based on multi-task mobilenets. Sensors, 19.10.3390/s19143200 ↗
42. He, J., Chen, J., Liu, J., and Li, H. (2019, January 6–8). A lightweight architecture for driver status monitoring via convolutional neural networks. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China.10.1109/ROBIO49542.2019.8961845 ↗
43. L-TLA: A Lightweight Driver Distraction Detection Method Based on Three-Level Attention Mechanisms
44. Gupta, K., Choubey, S., Yogeesh, N., William, P., and Kale, C.P. (2023, January 5–7). Implementation of motorist weariness detection system using a conventional object recognition technique. Proceedings of the 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India.10.1109/IDCIoT56793.2023.10052783 ↗
45. Driver distraction detection method based on continuous head pose estimation10.1155/2020/6616584 ↗
46. Driver mental fatigue detection based on head posture using new modified reLU-BiLSTM deep neural network10.1109/TITS.2021.3098309 ↗
47. Driver activity recognition for intelligent vehicles: A deep learning approach10.1109/TVT.2019.2908425 ↗
48. Ma, C., Wang, H., and Li, J. (2022, January 8–10). Driver behavior recognition based on attention module and bilinear fusion network. Proceedings of the Second International Conference on Digital Signal and Computer Communications (DSCC 2022), Changchun, China.10.1117/12.2641412 ↗
49. Driver behavior recognition via interwoven deep convolutional neural nets with multi-stream inputs10.1109/ACCESS.2020.3032344 ↗
50. A fuzzy-logic approach to dynamic bayesian severity level classification of driver distraction using image recognition10.1109/ACCESS.2020.2994811 ↗
51. Real-time driving distraction recognition through a wrist-mounted accelerometer10.1177/0018720821995000 ↗
52. Vision based detection of driver cell phone usage and food consumption10.1109/TITS.2020.3043145 ↗
53. EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate10.1016/j.clinph.2008.03.012 ↗
54. Bundele, M.M., and Banerjee, R. (2009, January 14–16). Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: A neural network approach. Proceedings of the 11th International Conference on Information Integration and Web-Based Applications & Services, Kuala Lumpur, Malaysia.10.1145/1806338.1806478 ↗
55. Driver fatigue detection through chaotic entropy analysis of cortical sources obtained from scalp EEG signals10.1109/TITS.2018.2890332 ↗
56. A temporal–spatial deep learning approach for driver distraction detection based on EEG signals10.1109/TASE.2021.3088897 ↗
57. Detection of driving fatigue by using noncontact EMG and ECG signals measurement system10.1142/S0129065714500063 ↗
58. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning10.1016/j.eswa.2015.05.028 ↗
59. Detection of train driver fatigue and distraction based on forehead EEG: A time-series ensemble learning method10.1109/TITS.2021.3125737 ↗
60. Yang, J., Chang, T.N., and Hou, E. (2010, January 7–10). Driver distraction detection for vehicular monitoring. Proceedings of the IECON 2010–36th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, USA.10.1109/IECON.2010.5675190 ↗
61. Driver distraction detection based on vehicle dynamics using naturalistic driving data10.1016/j.trc.2022.103561 ↗
62. Sun, Q., Wang, C., Guo, Y., Yuan, W., and Fu, R. (2020). Research on a cognitive distraction recognition model for intelligent driving systems based on real vehicle experiments. Sensors, 20.10.3390/s20164426 ↗
63. Fatigue detection system based on multi-source information source fusion
64. Du, Y., Raman, C., Black, A.W., Morency, L.P., and Eskenazi, M. (2018). Multimodal polynomial fusion for detecting driver distraction. arXiv.10.21437/Interspeech.2018-2011 ↗
65. Hypo-driver: A multiview driver fatigue and distraction level detection system
66. A review of sensory interactions between autonomous vehicles and drivers10.1016/j.sysarc.2023.102932 ↗
67. Wogalter, M.S., Mayhorn, C.B., and Laughery, K.R. (2021). Handbook of Human Factors and Ergonomics, John Wiley & Sons.
68. Improvement of autonomous vehicles trust through synesthetic-based multimodal interaction10.1109/ACCESS.2021.3059071 ↗
69. Intelligent In-Vehicle Interaction Technologies10.1002/aisy.202100122 ↗
70. Zhou, X., Zheng, J., and Zhang, W. (2022). Human-Automation Interaction: Transportation, Springer.
71. Awake at the wheel: How auto technology innovations present ongoing sleep challenges and new safety opportunities10.1093/sleep/zsad316 ↗
72. A survey of autonomous driving: Common practices and emerging technologies10.1109/ACCESS.2020.2983149 ↗
73. Autonomous driving ethics: From trolley problem to ethics of risk10.1007/s13347-021-00449-4 ↗
74. Potential cyberattacks on automated vehicles
75. Cyber threats facing autonomous and connected vehicles: Future challenges10.1109/TITS.2017.2665968 ↗
76. A secure adaptive control for cooperative driving of autonomous connected vehicles in the presence of heterogeneous communication delays and cyberattacks10.1109/TCYB.2019.2962601 ↗
77. Cyber-physical system-based path tracking control of autonomous vehicles under cyber-attacks10.1109/TII.2022.3206354 ↗
78. Connected and autonomous vehicles: A cyber-risk classification framework10.1016/j.tra.2018.06.033 ↗
79. Real-time sensor anomaly detection and identification in automated vehicles
80. Detection and isolation of sensor attacks for autonomous vehicles: Framework, algorithms, and validation10.1109/TITS.2021.3077015 ↗
81. Li, T., Shang, M., Wang, S., Filippelli, M., and Stern, R. (2022, January 8–12). Detecting stealthy cyberattacks on automated vehicles via generative adversarial networks. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.10.1109/ITSC55140.2022.9922128 ↗