基于随机森林的驾驶人驾驶习性辨识策略
|
摘要
深入理解驾驶人驾驶习性及其表征方法,对于实现在汽车自动驾驶、辅助驾驶等不同控制系统下的人机和谐交互具有重要意义。为此,本文中提出了一种基于随机森林模型的驾驶人驾驶习性辨识策略。搭建了驾驶人驾驶数据实车采集系统,在典型跟车驾驶工况下对驾驶人驾驶习性数据进行了实时采集;根据层次聚类理论,对驾驶人驾驶习性进行了标定;在此基础上,引入随机森林模型建立了驾驶人驾驶习性辨识策略,并进行了重要性分析、模型训练和测试分析。测试结果表明,本文提出的基于随机森林模型的驾驶人驾驶习性辨识策略能有效辨识驾驶人驾驶习性,模型整体精准度可达97.1%。
Understanding and identification of driver's driving style are of great significance to the human-machine harmonious interaction under different control systems such as automatic driving and assistant driving. A driving style identification strategy based on random forest model is proposed in this paper. Firstly, the driver's driving data acquisition system is set up. Based on that, the driving data of several drivers are collected in real time under typical car-following scenarios. According to hierarchical clustering theory, the driving style are "labeled". On this basis, a random forest model is introduced to establish driving style identification strategy, and importance analysis, model training and identification test are carried out. The test results show that the driving style identification strategy based on the random forest model can effectively identify driver's driving style and the overall accuracy of the model can reach 97.1%.
Understanding and identification of driver's driving style are of great significance to the human-machine harmonious interaction under different control systems such as automatic driving and assistant driving. A driving style identification strategy based on random forest model is proposed in this paper. Firstly, the driver's driving data acquisition system is set up. Based on that, the driving data of several drivers are collected in real time under typical car-following scenarios. According to hierarchical clustering theory, the driving style are "labeled". On this basis, a random forest model is introduced to establish driving style identification strategy, and importance analysis, model training and identification test are carried out. The test results show that the driving style identification strategy based on the random forest model can effectively identify driver's driving style and the overall accuracy of the model can reach 97.1%.
引文
[1] MARTINEZ C M, HEUCKE M, WANG F Y, et al. Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey[J]. IEEE Transactions on Intelligent Transportation Systems,2018,19(3):666-676.
[2] AKENA R, SCHMID F, BURROW M. Driving style for better fuel economy[J]. Transport,2017,170(3):131-139.
[3] WAHAB A, WEN T G, KAMARUDDIN N. Understanding driver behavior using multi-dimensional CMAC information[C]. Singapore: 6th IEEE International Conference on Communications & Signal Processing,2007.
[4] LU J, FILEV D, PRAKAH-ASANTE K, et al. From vehicle stability control to intelligent personal minder: real-time vehicle handling limit warning and driver style characterization[C]. IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems,2009.
[5] WANG J, LU M, LI K. Characterization of longitudinal driving behavior by measurable parameters[J]. Transportation Research Record,2010,2185(1):15-23.
[6] 毛锦.考虑驾驶风格的换道预警方法[D].西安:长安大学,2012.
[7] 石琴,仇多洋,周洁瑜.基于组合聚类法的行驶工况构建与精度分析[J].汽车工程,2012,34(2):164-169.
[8] 张淼,刘明周,扈静,等.基于K-均值聚类的汽车驾驶姿态偏好研究[J].汽车工程,2017,39(10):1187-1191.
[9] PANG B, YUE J, ZHAO G, et al. Statistical downscaling of temperature with the random forest model[J]. Advances in Meteorology,2017,Artide ID 7265178.
[10] GREGORUTTI B, MICHEL B, SAINT-PIERRE P. Correlation and variable importance in random forests[J]. Statistics and Computing,2017,27(3):659-678.
[11] HERRERA F. On the use of MapReduce for imbalanced big data using random forest[J]. Information Sciences An International Journal,2014,285:112-137.
[2] AKENA R, SCHMID F, BURROW M. Driving style for better fuel economy[J]. Transport,2017,170(3):131-139.
[3] WAHAB A, WEN T G, KAMARUDDIN N. Understanding driver behavior using multi-dimensional CMAC information[C]. Singapore: 6th IEEE International Conference on Communications & Signal Processing,2007.
[4] LU J, FILEV D, PRAKAH-ASANTE K, et al. From vehicle stability control to intelligent personal minder: real-time vehicle handling limit warning and driver style characterization[C]. IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems,2009.
[5] WANG J, LU M, LI K. Characterization of longitudinal driving behavior by measurable parameters[J]. Transportation Research Record,2010,2185(1):15-23.
[6] 毛锦.考虑驾驶风格的换道预警方法[D].西安:长安大学,2012.
[7] 石琴,仇多洋,周洁瑜.基于组合聚类法的行驶工况构建与精度分析[J].汽车工程,2012,34(2):164-169.
[8] 张淼,刘明周,扈静,等.基于K-均值聚类的汽车驾驶姿态偏好研究[J].汽车工程,2017,39(10):1187-1191.
[9] PANG B, YUE J, ZHAO G, et al. Statistical downscaling of temperature with the random forest model[J]. Advances in Meteorology,2017,Artide ID 7265178.
[10] GREGORUTTI B, MICHEL B, SAINT-PIERRE P. Correlation and variable importance in random forests[J]. Statistics and Computing,2017,27(3):659-678.
[11] HERRERA F. On the use of MapReduce for imbalanced big data using random forest[J]. Information Sciences An International Journal,2014,285:112-137.