[1] |
严新平, 张晖, 吴超仲, 等. 道路交通驾驶行为研究进展及其展望[J]. 交通信息与安全, 2013, 31(1):45-51.
|
|
YAN Xinping, ZHANG Hui, WU Chaozhong, et al. Research progress and prospect of road traffic driving behavio[J]. Journal of Transport Information and Safety, 2013, 31(1):45-51.
|
[2] |
张正. 基于车联网驾驶行为评分的安全驾驶卫士分析与设计[D]. 南京: 南京邮电大学, 2017.
|
|
ZHANG Zheng. The analysis and design of driving safety guards based on the score of driving behavior in vehicle networking[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2017.
|
[3] |
ELLISON A B, BLIEMER M C J, GREAVES S P. Evaluating changes in driver behaviour: a risk profiling approach[J]. Accident Analysis & Prevention, 2015, 75:298-309.
doi: 10.1016/j.aap.2014.12.018
|
[4] |
李平凡. 驾驶行为表征指标及分析方法研究[D]. 长春: 吉林大学, 2010.
|
|
LI Pingfan. Research on indices and analysis of driving behavior[D]. Changchun: Jilin University, 2017.
|
[5] |
SAIPRASERT C, PHOLPRASIT T, THAJCHAYAPONG S. Detection of driving events using sensory data on smartphone[J]. International Journal of Intelligent Transportation Systems Research, 2017, 15(1):17-28.
doi: 10.1007/s13177-015-0116-5
|
[6] |
GUO Feng, FANG Youjia. Individual driver risk assessment using naturalistic driving data[J]. Accident Analysis & Prevention, 2013, 61(6):3-9.
doi: 10.1016/j.aap.2012.06.014
|
[7] |
胥川, 裴赛君, 王雪松. 基于无侵入测量指标的个体差异化驾驶疲劳检测[J]. 中国公路学报, 2016, 29(10):118-125.
|
|
XU Chuan, PEI Saijun, WANG Xuesong. Driver drowsiness detection based on non-intrusive metrics considering individual difference[J]. China Journal of Highway and Transport, 2016, 29(10):118-125.
|
[8] |
秦雅琴, 李秋谷, 赵鹏燕, 等. 基于多分类Adaboost算法的驾驶人风险感知倾向研究[J]. 中国安全科学学报, 2022, 32(4):141-147.
doi: 10.16265/j.cnki.issn1003-3033.2022.04.021
|
|
QIN Yaqin, LI Qiugu, ZHAO Pengyan, et al. Research on risk perception tendency of drivers based on multi-class Ada-boost algorithm[J]. China Safety Science Journal, 2022, 32(4): 141-147.
doi: 10.16265/j.cnki.issn1003-3033.2022.04.021
|
[9] |
曾诚, 吴初娜, 孟兴凯. 驾驶人危险辨识进展研究[J]. 中国安全科学学报, 2016, 26(12): 74-79.
|
|
ZENG Cheng, WU Chu'na, MENG Xingkai. A review of progress in studying drivers' hazard perception[J]. China Safety Science Journal, 2016, 26(12): 74-79.
|
[10] |
乔洁, 徐鑫, 刘传攀, 等. 驾驶人危险感知能力影响因素及干预方式综述[J]. 中国安全科学学报, 2022, 32(2): 34-41.
doi: 10.16265/j.cnki.issn1003-3033.2022.02.006
|
|
QIAO Jie, XU Xin, LIU Chuanpan, et al. Review on affecting factors and intervention methods of drivers' hazard perception ability[J]. China Safety Science Journal, 2022, 32(2): 34-41.
doi: 10.16265/j.cnki.issn1003-3033.2022.02.006
|
[11] |
AKSAN N, SAGER L, HACKER S, et al. Individual differences in cognitive functioning predict effectiveness of a heads-up lane departure warning for younger and older drivers[J]. Accident Analysis & Prevention, 2017, 99: 171-183.
doi: 10.1016/j.aap.2016.11.003
|
[12] |
ENEV M, TAKAKUWA A, KOSCHER K, et al. Automobile driver fingerprinting[J]. Proceedings on Privacy Enhancing Technologies, 2015, 2016(1): 34-50.
doi: 10.1515/popets-2015-0029
|
[13] |
EBOLI L, MAZZULLA G, PUNGILLO G. Combining speed and acceleration to define car users' safe or unsafe driving behavior[J]. Transportation Research Part C: Emerging Technologies, 2016, 68: 113-125.
doi: 10.1016/j.trc.2016.04.002
|
[14] |
BIAN Yiyang, YANG Chen, ZHAO J, et al. Good drivers pay less: a study of usage-based vehicle insurance models[J]. Transportation Research Part A: Policy and Practice, 2018, 107: 20-34.
doi: 10.1016/j.tra.2017.10.018
|
[15] |
WILDE G J S. The theory of risk homeostasis: implications for safety and health[J]. Risk Analysis, 1982, 2(4): 209-225.
doi: 10.1111/risk.1982.2.issue-4
|
[16] |
ZHANG Xiaoxuan, LU Guangquan, CHENG Bo. Parameters calibration for car-following model based desired safety margin[C]. International Conference on Optoelectronics & Image Processing. IEEE, 2011: 97-100.
|
[17] |
吕能超, 任泽远, 段至诚, 等. Near-crash事件中驾驶人行为特征分析[J]. 中国安全科学学报, 2017, 27(6): 19-24.
doi: 10.16265/j.cnki.issn1003-3033.2017.06.004
|
|
LYU Nengchao, REN Zeyuan, DUAN Zhicheng, et al. Analysis of driving behavior characteristics of drivers in near-crash events[J]. China Safety Science Journal, 2017, 27(6): 19-24.
doi: 10.16265/j.cnki.issn1003-3033.2017.06.004
|
[18] |
张新生, 蔡宝泉. 基于改进随机森林模型的海底管道腐蚀预测[J]. 中国安全科学学报, 2021, 31(8): 69-74.
doi: 10.16265/j.cnki.issn1003-3033.2021.08.010
|
|
ZHANG Xinsheng, CAI Baoquan. Corrosion prediction of submarine pipelines based on improved random forest model[J]. China Safety Science Journal, 2021, 31(8): 69-74.
doi: 10.16265/j.cnki.issn1003-3033.2021.08.010
|