Research on Road Traffic Safety Risk Assessment Based on the Data of Radar Video Integrated Sensors
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Tang ZZ, Zhang TJ, He Y. Road Traffic Safety Evaluation. Beijing, China: People’s Communications Press; 2008.
Nguyen TH, Lu DN, Nguyen DN, Nguyen HN. Dynamic basic activity sequence matching method in abnormal driving pattern detection using smartphone sensors. Electronics. 2020;9(2):217. DOI: 10.3390/electronics9020217.
Mcgehee DV, et al. Extending parental mentoring using an event-triggered video intervention in rural teen drivers. Journal of Safety Research. 2007;38(2):215–227. DOI: 10.1016/j.jsr.2007.02.009.
Li F, Zhang H, Che H, Qiu X. Dangerous driving behavior detection using smartphone sensors. IEEE 19th international conference on intelligent transportation systems (ITSC). 1-4 Nov. 2016, Rio de Janeiro, Brazil. 2016. p. 1902-1907. DOI:10.1109/ITSC.2016.7795864.
Lu J, Wang K, Jiang YM. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory. Journal of Traffic and Transportation Engineering. 2020;20(6):227-235. DOI: 10.19818/j.cnki.1671-1637.2020.06.020.
Ma Y, et al. On-line aggressive driving identification based on in-vehicle kinematic parameters under naturalistic driving conditions. Transportation research part C: emerging technologies. 2020;114:554-571. DOI: 10.1016/j.trc.2020.02.028.
Li C, Liu Y. Abnormal driving behavior detection based on covariance Manifold and Logitboost. Laser & Optoelectronics Progress. 2018;55(11):338-345. DOI: 10.3788/LOP55.111503.
Shahverdy M, Fathy M, Berangi R, Sabokrou M. Driver behavior detection and classification using deep convolutional neural networks. Expert Systems with Applications. 2020;149:113240. DOI: 10.1016/j.eswa.2020.113240.
Hu J, et al. Abnormal driving detection based on normalized driving behavior. IEEE Transactions on Vehicular Technology. 2017;66(8):6645-6652. DOI: 10.1109/TVT.2017.2660497.
Hu J, Zhang X, Maybank S. Abnormal driving detection with normalized driving behavior data: A deep learning approach. IEEE transactions on vehicular technology. 2020;69(7):6943-6951. DOI:10.1109/TVT.2020.2993247.
Huang X, Sun J, Sun J. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks. Transportation Research Part C: Emerging Technologies. 2018;95:346–362. DOI: 10.1016/j.trc.2018.07.022.
Liu J, et al. One-dimensional convolutional neural network model for abnormal driving behaviors detection using smartphone sensors. International Conference on Networking Systems of AI (INSAI). 19-20 Nov. 2021, Shanghai, China. 2021.p. 143-150. DOI:10.1109/INSAI54028.2021.00035.
Chen S, et al. Vehicles driving behavior recognition based on transfer learning. Expert Systems with Applications. 2023;213:119254. DOI:10.1016/j.eswa.2022.119254.
Xiao W, et al. FDAN: Fuzzy deep attention networks for driver behavior recognition. Journal of Systems Architecture. 2024;147:103063. DOI:10.1016/j.sysarc.2023.103063.
Darsono AM, et al. Utilizing LSTM networks for the prediction of driver behavior. Przeglad Elektrotechniczny. 2024;100(04):182-185. DOI: 10.15199/48.2024.04.34.
Eren H, Makinist S, Akin E, Yilmaz A. Estimating driving behavior by a smartphone. IEEE 2012 IEEE Intelligent Vehicles Symposium (IV),3-7 Jun. 2012, Madrid, Spain. 2012. p. 234–239. DOI: 10.1109/IVS.2012.6232298.
Chen F, Wang J, Deng Y. Road safety risk evaluation by means of improved entropy TOPSIS–RSR. Safety science. 2015;79:39-54. DOI: 10.1016/j.ssci.2015.05.006.
Yan Y, et al. Driving risk assessment using driving behavior data under continuous tunnel environment. Traffic injury prevention. 2019;20(8):807-812. DOI:10.1080/15389588.2019.1675154.
Chen J, Wu ZC, Zhang J. Driving safety risk prediction using cost-sensitive with nonnegativity-constrained autoencoders based on imbalanced naturalistic driving data. IEEE transactions on intelligent transportation systems. 2019;20(12):4450-4465. DOI:10.1109/TITS.2018.2886280.
Cai X, et al. Road traffic safety risk estimation based on driving behavior and information entropy. China J. Highw. Transp. 2020;33(06):190-201. DOI:10.1155/2020/3024101.
Wang T, et al. Traffic risk assessment based on warning data. Journal of Advanced Transportation. 2022;2022(9):1-11. DOI:10.1155/2022/1191239.
Yang H, Zhao X, Luan S, Chai S. A traffic dynamic operation risk assessment method using driving behaviors and traffic flow data: An empirical analysis. Expert Systems with Applications. 2024;249:123619. DOI:10.1016/j.eswa.2024.123619.
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