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2023 | OriginalPaper | Chapter

A Survey of Driver Behavior Perception Methods for Human-Computer Hybrid Enhancement of Intelligent Driving

Authors : Jiwei Yi, Aimin Du, Zhongpan Zhu, Hongjun Ding

Published in: Proceedings of China SAE Congress 2021: Selected Papers

Publisher: Springer Nature Singapore

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Abstract

The subjective uncertainty of human-in-the-loop is a core issue for hybrid enhancement of human-computer shared driving that is the research hotspot of intelligent vehicle studies. Precise perception of driver behavior is a prerequisite and a significant method to break through the problem of human-in-the-loop uncertainties. Through literature comparison and analysis, we find that the objective evaluation method based on contact and non-contact sensors has received more attention from scholars in comparison with the subjective evaluation of driver behavior. However, there is no literature that addresses the new challenges of driver behavior perception under the condition of human-computer shared driving. Therefore the methods of driver behavior perception for hybrid enhancement of human-computer shared driving are summarized in this paper and the driver behavior perception based on visual and tactile multi-sensor fusion has been pointed out as the future research direction.

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Literature
1.
go back to reference Liu, F., Li, X., Lv, T., et al.: A review of driver fatigue detection: progress and prospect. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6. IEEE (2019) Liu, F., Li, X., Lv, T., et al.: A review of driver fatigue detection: progress and prospect. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6. IEEE (2019)
2.
go back to reference McDonald, A.D., Ferris, T.K., Wiener, T.A.: Classification of driver distraction: comprehensive analysis of feature generation, machine learning, and input measures. Hum. Factors (2019) McDonald, A.D., Ferris, T.K., Wiener, T.A.: Classification of driver distraction: comprehensive analysis of feature generation, machine learning, and input measures. Hum. Factors (2019)
3.
go back to reference Kaplan, S., Guvensan, M.A., Yavuz, A.G., et al.: Driver behavior analysis for safe driving: a survey. IEEE Trans. Intell. Transp. Syst. 16(6), 3017–3032 (2015)CrossRef Kaplan, S., Guvensan, M.A., Yavuz, A.G., et al.: Driver behavior analysis for safe driving: a survey. IEEE Trans. Intell. Transp. Syst. 16(6), 3017–3032 (2015)CrossRef
4.
go back to reference Chowdhury, A., Shankaran, R., Kavakli, M., et al.: Sensor applications and physiological features in drivers’ drowsiness detection: a review. IEEE Sens. J. 18(8), 3055–3067 (2018) CrossRef Chowdhury, A., Shankaran, R., Kavakli, M., et al.: Sensor applications and physiological features in drivers’ drowsiness detection: a review. IEEE Sens. J. 18(8), 3055–3067 (2018) CrossRef
5.
go back to reference Tayab Khan, M., Anwar, H., Ullah, F., et al.: Smart real-time video surveillance platform for drowsiness detection based on eyelid closure. Wirel. Commun. Mob. Comput. (2019) Tayab Khan, M., Anwar, H., Ullah, F., et al.: Smart real-time video surveillance platform for drowsiness detection based on eyelid closure. Wirel. Commun. Mob. Comput. (2019)
6.
go back to reference Zhang, X., et al.: Driver drowsiness detection using mixed-effect ordered Logit model considering time cumulative effect. Anal. Methods Accid. Res. 26 (2020) Zhang, X., et al.: Driver drowsiness detection using mixed-effect ordered Logit model considering time cumulative effect. Anal. Methods Accid. Res. 26 (2020)
7.
go back to reference Flint, A., Raben, A., Blundell, J.E., et al.: Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies. Int. J. Obes. 24(1), 38–48 (2000)CrossRef Flint, A., Raben, A., Blundell, J.E., et al.: Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies. Int. J. Obes. 24(1), 38–48 (2000)CrossRef
8.
go back to reference Hsberg, E.: Dimensions of fatigue during radiotherapy–An application of the Swedish Occupational Fatigue Inventory (SOFI) on cancer patients. Acta Oncol. 40(1), 37–43 (2001)CrossRef Hsberg, E.: Dimensions of fatigue during radiotherapy–An application of the Swedish Occupational Fatigue Inventory (SOFI) on cancer patients. Acta Oncol. 40(1), 37–43 (2001)CrossRef
9.
go back to reference Wierwille, W.W., Ellsworth, L.A.: Evaluation of driver drowsiness by trained raters. Accid. Anal. Prev. 26(5), 571–581 (1994)CrossRef Wierwille, W.W., Ellsworth, L.A.: Evaluation of driver drowsiness by trained raters. Accid. Anal. Prev. 26(5), 571–581 (1994)CrossRef
10.
go back to reference Takei, Y., Furukawa, Y.: Estimate of driver’s fatigue through steering motion. In: 2005 IEEE International Conference on Systems, Man and Cybernetics. IEEE (2005) Takei, Y., Furukawa, Y.: Estimate of driver’s fatigue through steering motion. In: 2005 IEEE International Conference on Systems, Man and Cybernetics. IEEE (2005)
11.
go back to reference Choi, S., Kim, J., Kwak, D., et al.: Analysis and classification of driver behavior using in-vehicle CAN-bus information. In: Workshop on DSP for In-Vehicle and Mobile Systems (2007) Choi, S., Kim, J., Kwak, D., et al.: Analysis and classification of driver behavior using in-vehicle CAN-bus information. In: Workshop on DSP for In-Vehicle and Mobile Systems (2007)
12.
go back to reference Lattanzi, E., Castellucci, G., Freschi, V.: Improving machine learning identification of unsafe driver behavior by means of sensor fusion. Appl. Sci.-Basel 10(18) (2020) Lattanzi, E., Castellucci, G., Freschi, V.: Improving machine learning identification of unsafe driver behavior by means of sensor fusion. Appl. Sci.-Basel 10(18) (2020)
13.
go back to reference Shahverdy, M., Fathy, M., Berangi, R., et al.: Driver behavior detection and classification using deep convolutional neural networks. Expert Syst. Appl. 149 (2020) Shahverdy, M., Fathy, M., Berangi, R., et al.: Driver behavior detection and classification using deep convolutional neural networks. Expert Syst. Appl. 149 (2020)
14.
go back to reference Arefnezhad, S., Samiee, S., Eichberger, A., et al.: Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures. Expert Syst. Appl. 162 (2020) Arefnezhad, S., Samiee, S., Eichberger, A., et al.: Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures. Expert Syst. Appl. 162 (2020)
15.
go back to reference Jung, S., Shin, H., Chung, W.: Highly sensitive driver health condition monitoring system using nonintrusive active electrodes. Sens. Actuators B-Chem. 171, 691–698 (2012)CrossRef Jung, S., Shin, H., Chung, W.: Highly sensitive driver health condition monitoring system using nonintrusive active electrodes. Sens. Actuators B-Chem. 171, 691–698 (2012)CrossRef
16.
go back to reference Yu, L., Sun, X., Zhang, K.: Driving Distraction Analysis by ECG Signals: An Entropy Analysis, pp. 258–264 (2011) Yu, L., Sun, X., Zhang, K.: Driving Distraction Analysis by ECG Signals: An Entropy Analysis, pp. 258–264 (2011)
17.
go back to reference Lee, B.G., Park, J., Pu, C.C., et al.: Smartwatch-based driver vigilance indicator with kernel-fuzzy-C-means-wavelet method. IEEE Sens. J. 16(1), 242–253 (2016)CrossRef Lee, B.G., Park, J., Pu, C.C., et al.: Smartwatch-based driver vigilance indicator with kernel-fuzzy-C-means-wavelet method. IEEE Sens. J. 16(1), 242–253 (2016)CrossRef
18.
go back to reference Chui, K.T., Lytras, M.D., Liu, R.W.: A generic design of driver drowsiness and stress recognition using MOGA optimized deep MKL-SVM. Sensors 20(5) (2020) Chui, K.T., Lytras, M.D., Liu, R.W.: A generic design of driver drowsiness and stress recognition using MOGA optimized deep MKL-SVM. Sensors 20(5) (2020)
19.
go back to reference Chai, R., Naik, G.R., Nguyen, T.N., et al.: Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J. Biomed. Health Inform. 21(3), 715–724 (2017)CrossRef Chai, R., Naik, G.R., Nguyen, T.N., et al.: Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J. Biomed. Health Inform. 21(3), 715–724 (2017)CrossRef
20.
go back to reference Chai, R., Ling, S.H., San, P.P., et al.: Improving EEG-based driver fatigue classification using sparse-deep belief networks. Front. Neurosci. 11(103) (2017) Chai, R., Ling, S.H., San, P.P., et al.: Improving EEG-based driver fatigue classification using sparse-deep belief networks. Front. Neurosci. 11(103) (2017)
21.
go back to reference Shangguan, P., Qiu, T., Liu, T., et al.: Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state. Physiol. Meas. 41(12500412) (2020) Shangguan, P., Qiu, T., Liu, T., et al.: Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state. Physiol. Meas. 41(12500412) (2020)
22.
go back to reference Suman, D., Malini, M., Anchuri, S.: EOG based vigilance monitoring system. In: 2015 Annual IEEE India Conference (INDICON). IEEE (2015) Suman, D., Malini, M., Anchuri, S.: EOG based vigilance monitoring system. In: 2015 Annual IEEE India Conference (INDICON). IEEE (2015)
23.
go back to reference Jiao, Y., Deng, Y., Luo, Y., et al.: Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing 408, 100–111 (2020)CrossRef Jiao, Y., Deng, Y., Luo, Y., et al.: Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing 408, 100–111 (2020)CrossRef
24.
go back to reference Sahayadhas, A., Sundaraj, K., Murugappan, M., et al.: Physiological signal based detection of driver hypovigilance using higher order spectra. Expert Syst. Appl. 42(22), 8669–8677 (2015)CrossRef Sahayadhas, A., Sundaraj, K., Murugappan, M., et al.: Physiological signal based detection of driver hypovigilance using higher order spectra. Expert Syst. Appl. 42(22), 8669–8677 (2015)CrossRef
25.
go back to reference Qi, M., Xie, P., Zhang, Y., et al.: Driver fatigue assessment based on the feature fusion and transfer learning of EEG and EMG, pp. 1314–1317 (2018) Qi, M., Xie, P., Zhang, Y., et al.: Driver fatigue assessment based on the feature fusion and transfer learning of EEG and EMG, pp. 1314–1317 (2018)
26.
go back to reference Boon-Leng, L., Dae-Seok, L., Boon-Giin, L.: Mobile-based wearable-type of driver fatigue detection by GSR and EMG. In: Tencon IEEE Region 10 Conference. IEEE (2016) Boon-Leng, L., Dae-Seok, L., Boon-Giin, L.: Mobile-based wearable-type of driver fatigue detection by GSR and EMG. In: Tencon IEEE Region 10 Conference. IEEE (2016)
27.
go back to reference Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)CrossRef Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)CrossRef
28.
go back to reference Choi, M., Koo, G., Seo, M., et al.: Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Trans. Instrum. Meas. 67(3), 634–645 (2017)CrossRef Choi, M., Koo, G., Seo, M., et al.: Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Trans. Instrum. Meas. 67(3), 634–645 (2017)CrossRef
29.
go back to reference Lee, B.G., Chung, W.Y.: Wearable glove-type driver stress detection using a motion sensor. IEEE Trans. Intell. Transp. Syst. 18(7), 1835–1844 (2016)CrossRef Lee, B.G., Chung, W.Y.: Wearable glove-type driver stress detection using a motion sensor. IEEE Trans. Intell. Transp. Syst. 18(7), 1835–1844 (2016)CrossRef
30.
go back to reference Zhao, Q., Jiang, J., Lei, Z., et al.: Detection method of eyes opening and closing ratio for driver’s fatigue monitoring. IET Intell. Transp. Syst. 15(1), 31–42 (2021)CrossRef Zhao, Q., Jiang, J., Lei, Z., et al.: Detection method of eyes opening and closing ratio for driver’s fatigue monitoring. IET Intell. Transp. Syst. 15(1), 31–42 (2021)CrossRef
31.
go back to reference Yang, L., Dong, K., Dmitruk, A.J., et al.: A dual-cameras-based driver gaze mapping system with an application on non-driving activities monitoring. IEEE Trans. Intell. Transp. Syst. 21(10), 4318–4327 (2020)CrossRef Yang, L., Dong, K., Dmitruk, A.J., et al.: A dual-cameras-based driver gaze mapping system with an application on non-driving activities monitoring. IEEE Trans. Intell. Transp. Syst. 21(10), 4318–4327 (2020)CrossRef
32.
go back to reference Adi, K., Widodo, C.E., Widodo, A.P., et al.: Monitoring system of drowsiness and lost focused driver using Raspberry Pi. Iran. J. Public Health 49(9), 1675–1682 (2020) Adi, K., Widodo, C.E., Widodo, A.P., et al.: Monitoring system of drowsiness and lost focused driver using Raspberry Pi. Iran. J. Public Health 49(9), 1675–1682 (2020)
33.
go back to reference Using driver’s head movements evolution as a drowsiness indicator. In: IEEE Intelligent Vehicles Symposium, pp. 616–621. IEEE (2003) Using driver’s head movements evolution as a drowsiness indicator. In: IEEE Intelligent Vehicles Symposium, pp. 616–621. IEEE (2003)
34.
go back to reference Sunagawa, M., Shikii, S., Nakai, W., et al.: Comprehensive drowsiness level detection model combining multimodal information. IEEE Sens. J. 20(7), 3709–3717 (2020)CrossRef Sunagawa, M., Shikii, S., Nakai, W., et al.: Comprehensive drowsiness level detection model combining multimodal information. IEEE Sens. J. 20(7), 3709–3717 (2020)CrossRef
35.
go back to reference Dong, Y., Hu, Z., Uchimura, K., et al.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)CrossRef Dong, Y., Hu, Z., Uchimura, K., et al.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)CrossRef
36.
go back to reference Zhao, M., Beurier, G., Wang, H., Wang, X.: In vehicle diver postural monitoring using a depth camera Kinect. SAE Technical Paper 2018-01-0505 (2018) Zhao, M., Beurier, G., Wang, H., Wang, X.: In vehicle diver postural monitoring using a depth camera Kinect. SAE Technical Paper 2018-01-0505 (2018)
37.
go back to reference Hu, C., Zhang, Y., Wu, F., et al.: Toward driver face recognition in the intelligent traffic monitoring systems. IEEE Trans. Intell. Transp. Syst. 21(12), 4958–4971 (2020)CrossRef Hu, C., Zhang, Y., Wu, F., et al.: Toward driver face recognition in the intelligent traffic monitoring systems. IEEE Trans. Intell. Transp. Syst. 21(12), 4958–4971 (2020)CrossRef
38.
go back to reference Liu, X., Xu, F., Fujimura, K.: Real-time eye detection and tracking for driver observation under various light conditions. In: Intelligent Vehicle Symposium. IEEE (2002) Liu, X., Xu, F., Fujimura, K.: Real-time eye detection and tracking for driver observation under various light conditions. In: Intelligent Vehicle Symposium. IEEE (2002)
39.
go back to reference Samiee, S., Azadi, S., Kazemi, R., et al.: Data fusion to develop a driver drowsiness detection system with robustness to signal loss. Sensors 14(9), 17832–17847 (2014)CrossRef Samiee, S., Azadi, S., Kazemi, R., et al.: Data fusion to develop a driver drowsiness detection system with robustness to signal loss. Sensors 14(9), 17832–17847 (2014)CrossRef
41.
go back to reference Cabrall, C.D., Eriksson, A., Dreger, F., et al.: How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas. Theor. Issues Ergon. Sci. 20(3), 332–365 (2019)CrossRef Cabrall, C.D., Eriksson, A., Dreger, F., et al.: How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas. Theor. Issues Ergon. Sci. 20(3), 332–365 (2019)CrossRef
42.
go back to reference Cardone, D., Perpetuini, D., Filippini, C., et al.: Driver stress state evaluation by means of thermal imaging: a supervised machine learning approach based on ECG signal. Appl. Sci.-Basel 10(567316) (2020) Cardone, D., Perpetuini, D., Filippini, C., et al.: Driver stress state evaluation by means of thermal imaging: a supervised machine learning approach based on ECG signal. Appl. Sci.-Basel 10(567316) (2020)
43.
go back to reference Lu, X., Zheng, L., Zhang, H., et al.: Stretchable, transparent triboelectric nanogenerator as a highly sensitive self-powered sensor for driver fatigue and distraction monitoring. Nano Energy 78(105359) (2020) Lu, X., Zheng, L., Zhang, H., et al.: Stretchable, transparent triboelectric nanogenerator as a highly sensitive self-powered sensor for driver fatigue and distraction monitoring. Nano Energy 78(105359) (2020)
44.
go back to reference Martinez, C.M., Heucke, M., Wang, F.Y., et al.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transp. Syst. PP(99), 1–11 (2018) Martinez, C.M., Heucke, M., Wang, F.Y., et al.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transp. Syst. PP(99), 1–11 (2018)
45.
go back to reference Yan, C., Xie, H., Yang, D., et al.: Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. PP(99), 1–12 (2017) Yan, C., Xie, H., Yang, D., et al.: Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. PP(99), 1–12 (2017)
46.
go back to reference Ji, X., Zhang, G., Chen, X., et al.: Multi-perspective tracking for intelligent vehicle. IEEE Trans. Intell. Transp. Syst. 19(2), 518–529 (2018)CrossRef Ji, X., Zhang, G., Chen, X., et al.: Multi-perspective tracking for intelligent vehicle. IEEE Trans. Intell. Transp. Syst. 19(2), 518–529 (2018)CrossRef
47.
go back to reference Xiong, G., Kang, Z., Li, H., et al.: Decision-making of lane change behavior based on RCS for automated vehicles in the real environment. In: 2018 IEEE Intelligent Vehicles Symposium (IV) (2018) Xiong, G., Kang, Z., Li, H., et al.: Decision-making of lane change behavior based on RCS for automated vehicles in the real environment. In: 2018 IEEE Intelligent Vehicles Symposium (IV) (2018)
48.
go back to reference Song, W., Yi, Y., Fu, M., et al.: Real-time obstacles detection and status classification for collision warning in a vehicle active safety system. IEEE Trans. Intell. Transp. Syst. 19(3), 758–773 (2018)CrossRef Song, W., Yi, Y., Fu, M., et al.: Real-time obstacles detection and status classification for collision warning in a vehicle active safety system. IEEE Trans. Intell. Transp. Syst. 19(3), 758–773 (2018)CrossRef
49.
go back to reference Wang, X.Y., Wang, Q.D., Gao, Z.G., et al.: Man-machine shared driving based lane departure avoidance control. Automot. Eng. 039(007), 839–848 (2017) Wang, X.Y., Wang, Q.D., Gao, Z.G., et al.: Man-machine shared driving based lane departure avoidance control. Automot. Eng. 039(007), 839–848 (2017)
50.
go back to reference Su, C., Deng, W., Sun, H., et al.: Forward collision avoidance systems considering driver’s driving behavior recognized by Gaussian mixture model. In: IEEE Intelligent Vehicles Symposium. IEEE (2017) Su, C., Deng, W., Sun, H., et al.: Forward collision avoidance systems considering driver’s driving behavior recognized by Gaussian mixture model. In: IEEE Intelligent Vehicles Symposium. IEEE (2017)
51.
go back to reference Zhao, Y.Q., Zhang, X.L., Zhang, W.X., et al.: Minimum time overtaking problem of vehicle handling inverse dynamics based on two kinds of safe distances. Chin. J. Mech. Eng. 31(1) (2018) Zhao, Y.Q., Zhang, X.L., Zhang, W.X., et al.: Minimum time overtaking problem of vehicle handling inverse dynamics based on two kinds of safe distances. Chin. J. Mech. Eng. 31(1) (2018)
52.
go back to reference Mühlbacher-Karrer, S., Mosa, A.H., Faller, L.M., et al.: A driver state detection system, combining a capacitive hand detection sensor with physiological sensors. IEEE Trans. Instrum. Meas. 66(4), 624–636 (2017)CrossRef Mühlbacher-Karrer, S., Mosa, A.H., Faller, L.M., et al.: A driver state detection system, combining a capacitive hand detection sensor with physiological sensors. IEEE Trans. Instrum. Meas. 66(4), 624–636 (2017)CrossRef
53.
go back to reference Jung, S.J., Shin, H.S., Chung, W.Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transp. Syst. 8(1), 43–50 (2014)CrossRef Jung, S.J., Shin, H.S., Chung, W.Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transp. Syst. 8(1), 43–50 (2014)CrossRef
54.
go back to reference Kuiper, R.J., Heck, D.J.F., Kuling, I.A., et al.: Evaluation of haptic and visual cues for repulsive or attractive guidance in nonholonomic steering tasks. IEEE Trans. Hum.-Mach. Syst. 46(5), 672–683 (2016)CrossRef Kuiper, R.J., Heck, D.J.F., Kuling, I.A., et al.: Evaluation of haptic and visual cues for repulsive or attractive guidance in nonholonomic steering tasks. IEEE Trans. Hum.-Mach. Syst. 46(5), 672–683 (2016)CrossRef
55.
go back to reference Hori, C., Hori, T., Lee, T.Y., et al.: Attention-based multimodal fusion for video description. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4193–4202 (2017) Hori, C., Hori, T., Lee, T.Y., et al.: Attention-based multimodal fusion for video description. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4193–4202 (2017)
Metadata
Title
A Survey of Driver Behavior Perception Methods for Human-Computer Hybrid Enhancement of Intelligent Driving
Authors
Jiwei Yi
Aimin Du
Zhongpan Zhu
Hongjun Ding
Copyright Year
2023
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-3842-9_58

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