최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.26 no.11, 2021년, pp.173 - 182
Han, Sangkon (Dept. of Computer Science Engineering, Pusan National University) , Choi, Jung-In (Dept. of Applied Artificial Intelligence, Ajou University)
In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analy...
Ministry of Land, Monthly vehicle registration statistics. Available: www.molt.go.kr.
National Highway Traffic Safety Administration, "Preliminary statement of policy concerning automated vehicles. washington, dc",. Available: www.nhtsa.gov/staticfiles/rulema king/pdf/Automated_Vehicles_Policy.pdf.
A. Kashevnik, R. Shchedrin, C. Kaiser and A. Stocker, "Driver Distraction Detection Methods: A Literature Review and Framework," in IEEE Access, vol. 9, pp. 60063-60076, 2021, DOI: 10.1109/ACCESS.2021.3073599.
Kaggle. State Farm Distracted Driver Detection. Available: https://www.kaggle.com/c/state-farm-distracted-driver-detection/
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva and A. Torralba, "Learning deep features for discriminative localization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921-2929.
G. Gunduz, C. Yaman, A. U. Peker and T. Acarman, "Prediction of Risk Generated by Different Driving Patterns and Their Conflict Redistribution," Tiv, vol. 3, (1), pp. 71-80, Mar, 2018. DOI: 10.1109/TIV.2017.2788203.
M. N. Azadani and A. Boukerche, "Driving Behavior Analysis Guidelines for Intelligent Transportation Systems," IEEE Transactions on Intelligent Transportation Systems, pp. 1-19, 2021. DOI: 10.1109/TITS.2021.3076140.
M. Costa, D. Oliveira, S. Pinto and A. Tavares, "Detecting Driver's Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System," Journal of Artificial Intelligence and Soft Computing Research, vol. 9, (4), pp. 247-266, Oct 1, 2019. DOI: 10.2478/jaiscr-2019-00 07.
S. Kim, "A Study of Driver's Visual and Auditory Task Distraction using Virtual Reality Driving Simulator," Master Thesis, Soongsil University, Korea, 2018.
H.J. Kim, H.J. Lim and J.H. Yang, "Design and Evaluation of Alert Threshold for Takeover Request in Partial Autonomous Vehicles considering Human Factors," in Proceedings of Conference, Korean Society of Automotive Engineers, 2016.
N. J. Dunn, T. A. Dingus, S. Soccolich and W. J. Horrey, "Investigating the impact of driving automation systems on distracted driving behaviors," Accident Analysis & Prevention, vol. 156, pp. 106152, 2021. DOI: 10.1016/j.aap. 2021.106152.
B. Wagner, F. Taffner, S. Karaca and L. Karge, "Vision Based Detection of Driver Cell Phone Usage and Food Consumption," IEEE Transactions on Intelligent Transportation Systems, 2021.
X. Zeng and J. Wang, "A stochastic driver pedal behavior model incorporating road information," IEEE Transactions on Human-Machine Systems, vol. 47, (5), pp. 614-624, 2017.
H. J. Chung, "A Study on Effect of Women Driver's Dangerous Behavior based on the Theory of Planned Behavior(TPB)," Doctoral Thesis, Myongji University, Korea, 2013.
Q. Hua, L. Jin, Y. Jiang, M. Gao and B. Guo, "Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment," Advances in Civil Engineering, vol. 2021, pp. 1-16, Mar 4, 2021. DOI: 10.1155/2021/6676807.
Wencai Sun, Yihao Si, Mengzhu Guo and Shiwu Li, "Driver Distraction Recognition Using Wearable IMU Sensor Data," Sustainability (Basel, Switzerland), vol. 13, (1342), pp. 1342, Jan 1, 2021. DOI: 10.3390/su13031342.
Y. Zhang, Y. Chen and C. Gao, "Deep unsupervised multi-modal fusion network for detecting driver distraction," Neurocomputing (Amsterdam), vol. 421, pp. 26-38, Jan 15, 2021. DOI: 10.1016/j.neucom.2020.09.023.
A. Lobo, S. Ferreira and A. Couto, "Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research," Sensors (Basel, Switzerland), vol. 20, (14), pp. 3836, Jul 9, 2020. DOI: 10.3390/s20143836.
M. Dua, Shakshi, R. Singla, S. Raj and A. Jangra, "Deep CNN models-based ensemble approach to driver drowsiness detection," Neural Computing & Applications, vol. 33, (8), pp. 3155-3168, Apr, 2021. DOI: 10.1007/s00521-020-05209- 7.
C. V. Hari and P. Sankaran, "Driver distraction analysis using face pose cues," Expert Systems with Applications, vol. 179, pp. 115036, Oct 1, 2021. DOI: 10.1016/j.eswa.202 1.115036.
Z. Zhao, S. Xia, X. Xu, L. Zhang, H. Yan, Y. Xu and Z. Zhang, "Driver Distraction Detection Method Based on Continuous Head Pose Estimation," Computational Intelligence and Neuroscience, vol. 2020, Nov 29, 2020. DOI: 10.1155/2020/9606908.
L. Li, B. Zhong, C. Hutmacher, Y. Liang, W. J. Horrey and X. Xu, "Detection of driver manual distraction via image-based hand and ear recognition," Accident Analysis and Prevention, vol. 137, pp. 105432, Mar, 2020. DOI: 10.1016/j.aap.2020.105432.
F. Vicente, Zehua Huang, Xuehan Xiong, F. De la Torre, Wende Zhang and D. Levi, "Driver Gaze Tracking and Eyes Off the Road Detection System," IEEE Transactions on Intelligent Transportation Systems, vol. 16, (4), pp. 2014-2027, Aug, 2015. DOI: 10.1109/TITS.2015.2396031.
A. Bhatt, V. Dave, Y. Panchamia and P. Thakre, "Analyzing behavioral attributes of drivers and implementing safe driving model," in 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2017, pp. 228-232.
A. Koesdwiady, R. Soua, F. Karray and M. S. Kamel, "Recent Trends in Driver Safety Monitoring Systems: State of the Art and Challenges," IEEE Transactions on Vehicular Technology, vol. 66, (6), pp. 4550-4563, Jun, 2017. DOI: 10.1109/TVT.2016.2631604.
M. Raja, V. Ghaderi and S. Sigg, "WiBot! in-vehicle behaviour and gesture recognition using wireless network edge," Proceeding of IEEE 38th International Conference on Distributed Computing Systems (ICDCS), in Jul 2018, pp. 376-387.
G. Li, W. Yan, S. Li, X. Qu, W. Chu and D. Cao, "A Temporal-Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals," IEEE Transactions on Automation Science and Engineering, pp. 1-13, Jun 23, 2021. DOI: 10.1109/TASE.2021.3088897.
M. Karthaus, E. Wascher and S. Getzmann, "Distraction in the Driving Simulator: An Event-Related Potential (ERP) Study with Young, Middle-Aged, and Older Drivers," Safety (Basel), vol. 7, (2), pp. 36, May 11, 2021. DOI: 10.3390/safety7020036.
S. D. Ki, "An analysis on the effects of the driver distraction on the car accidents," Insurance and Financial Research, vol. 22, (3), pp. 3-32, 2011.
N. Arbabzadeh and M. Jafari, "A Data-Driven Approach for Driving Safety Risk Prediction Using Driver Behavior and Roadway Information Data," IEEE transactions on intelligent transportation systems, vol. 19, (2), pp. 446-460, Feb, 2018. DOI: 10.1109/TITS.2017.2700869.
G. H. Choi, K. Lim and S. B. Pan, "Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles," Sensors (Basel), vol. 21, (1), pp. 10.3390/s21010202, December 30, 2020. . DOI: https://doi.org/10.3390/s21010202.
Y. Xing, C. Lv, Z. Zhang, H. Wang, X. Na, D. Cao, E. Velenis and F. Y. Wang, "Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition," IEEE Transactions on Computational Social Systems, Vol. 5, No. 1, pp. 95-108, March, 2018.
M. Mori, C. Miyajima, P. Angkititrakul, T. Hirayama, Y. Li, N. Kitaoka, and K. Takeda, "Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles," Proceeding of 2012 15th International IEEE Conference on Intelligent Transportation Systems, in Sep 2012, pp. 644-647.
J. H. L. Hansen, C. Busso, Yang Zheng and A. Sathyanarayana, "Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed," EEE Signal Processing Magazine, vol. 34, pp. 130-142, Jul. 2017.
N. Li, and C. Busso, "Predicting perceived visual and cognitive distractions of drivers with multimodal features," IEEE Transactions on Intelligent Transportation Systems, vol. 16, (1), pp. 51-65, Feb, 2015. DOI: 10.1109/TITS.2 014.2324414.
C. Huisingh, R. Griffin, and G. McGwin, "The prevalence of distraction among passenger vehicle drivers: a roadside observational approach," Traffic injury prevention, vol. 16, (2), pp. 140-146, Feb 17, 2015. DOI: 10.1080/15389588.20 14.916797.
C. A. Pickering, K. J. Burnham, and M. J. Richardson, "A research study of hand gesture recognition technologies and applications for human vehicle interaction," Proceeding of 3rd Institution of Engineering and Technology conference on Automotive Electronics, pp. 1-15, Nov, 2007.
W. Yan, S. Peng, C. Li, and L. Yang, "Impact to Longitude Velocity Control of Autonomous Vehicle from Human Driver's Distraction Behavior," Proceeding of IEEE 86th Vehicular Technology Conference (VTC-Fall), in Sep 2017, pp. 1-5.
SAE International Releases Updated Visual Chart for Its "Levels of Driving Automation" Standard for Self-Driving Vehicles, https://www.sae.org/news/press-room/2018/12/sae-int ernational-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles.
E. Pakdamanian, S. Sheng, S. Baee, S. Heo, S. Kraus and L. Feng, "Deeptake: Prediction of driver takeover behavior using multimodal data," in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-14, 2021.
Waymo. "Waymo Open Dataset." Available: https://waymo.com/open/data/.
Weconomy,. Self-Driving Vehicle Hits Bicyclist. Available: http://www.hani.co.kr/arti/economy/it/838665.html.
H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, "Speeded-up robust features (SURF)," Comput. Vision Image Understanding, vol. 110, (3), pp. 346-359, 2008.
D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, (2), pp. 91-110, 2004.
K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, (1), pp. 1929-1958, 2014.
D. Scherer, A. Muller and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," in International Conference on Artificial Neural Networks, 2010, pp. 92-101.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.