A Research on Adaptive Lane Change Warning Algorithm Based on Driver Characteristics

被引：0
作者：
Liu Z. [1 ]
Han J. [1 ]
Ni J. [1 ]
机构：
[1] School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang
来源：
Qiche Gongcheng/Automotive Engineering | 2019年 / 41卷 / 04期
关键词：
Adaptive driver characteristics; Fuzzy logic; Information entropy; Lane change warning; Maximum likelihood estimation;
D O I：
10.19562/j.chinasae.qcgc.2019.04.012
中图分类号：
学科分类号：
摘要：
A lane-changing hazard perception model based on the behavior characteristics of the driver is established, and a new algorithm is proposed with on-line parameter identification and adjustable threshold. By using the fuzzy logic method, the influence of the surrounding vehicles on lane change is determined by the speed correlation degree, the safety factor and the lateral deviation to modify hazard perception model parameters. Then the model parameters are on-line identified by recursive maximum likelihood estimation, and the real-time risk assessment value is obtained. Finally, based on the information entropy, the optimal alarm threshold is searched, and the real-time evaluation value is compared with the alarm threshold to judge the alarm state of the system. Verification results using natural driving behavior data from real-car experiments show that the accuracy of the adaptive warning model is 92.1% and the time to predict the state of danger can be advanced by 0.3-1s, which accords with the psychological expectation and practical operating characteristics of the driver. © 2019, Society of Automotive Engineers of China. All right reserved.
引用
收藏
页码：440 / 446and454
相关论文
共 9 条
  • [1] National motor vehicle crash causation survey: report to congress, (2008)
  • [2] Gong S., Du L., Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp, Transportation Research Part B Methodological, 84, pp. 1-30, (2016)
  • [3] Wang C., Fu R., Zhang Q., Et al., Research on parameter TTC characteristics of lane change warning system, China Journal of Highway and Transport, 28, 8, (2015)
  • [4] Minderhoud M.M., Bovy P.H.L., Extended time-to-collision measures for road traffic safety assessment, Accident Analysis & Prevention, 33, 1, pp. 89-97, (2001)
  • [5] Schmidt K.W., Cooperative adaptive cruise control for vehicle following during lane changes, IFAC-Papers Online, 50, 1, pp. 12582-12587, (2017)
  • [6] Saigo S., Raksincharoensak P., Nagai M., Estimation of driving performance level using longitudinal and lateral driver models, IFAC Proceedings Volumes, 46, 21, pp. 145-150, (2013)
  • [7] Nie J., Zhang J., Ding W., Et al., Decentralized cooperative lane-changing decision-making for connected autonomous vehicles, IEEE Access, 4, pp. 9413-9420, (2016)
  • [8] Bocklisch F., Bocklisch S.F., Beggiato M., Et al., Adaptive fuzzy pattern classification for the online detection of driver lane change intention, Neurocomputing, 262, 1, pp. 148-158, (2017)
  • [9] Abdi H., Signal detection theory., International Encyclopedia of Education, pp. 407-410, (2010)
1