基于驾驶员操纵特性和交通环境状态的换道行为预测
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摘要
为排除驾驶员在换道过程中存在的交通安全隐患,基于驾驶员操纵特性和交通环境状态分析,提出了一种能有效预测驾驶员换道行为的方法。依托自然道路条件下的实车实验,将驾驶员视觉特性与数据分析相结合,确定了意图时窗宽度为5 s。依据在车道保持与意图换道两阶段的操纵特性和交通环境等参数差异化分析,构建了换道行为预测的表征参数体系。引入二元Logistic模型,确定各参数的回归系数,并进行换道行为预测。研究结果表明:该模型可至少提前2. 5 s预测出换道行为;且换道行为起始时刻的预测精度为96. 34%。与基于视觉特性和转向灯状态预测换道行为相比,具有更高的准确率和更优的时序性。
In order to eliminate potential traffic safety during the lane-changing process,based on the analysis of handling characteristics and traffic environment states,a new method to identify drivers' lane-changing behavior was proposed. Relying on the real-world experiments,the drivers' visual characteristics and data analysis were combined to determine the intent window width of 5 s. Depending on the difference analysis of the handling characteristics and traffic environment between the lane-keeping and lane-changing intent stage,the characterization index system for predict lane-changing behavior was constructed. The two element Logistic model was introduced to determined the regression coefficient of each parameter and predict the lane-changing behavior. The research result show that the model may predict drivers' lane-changing behavior for at least 2. 5 s in advance,and the prediction accuracy of the start time of the lane-changing behavior is 96. 34%. The accuracy and time series characteristics of the model are superior to the use of turn signals and visual characteristics in predicting lane-changing behaviour.
In order to eliminate potential traffic safety during the lane-changing process,based on the analysis of handling characteristics and traffic environment states,a new method to identify drivers' lane-changing behavior was proposed. Relying on the real-world experiments,the drivers' visual characteristics and data analysis were combined to determine the intent window width of 5 s. Depending on the difference analysis of the handling characteristics and traffic environment between the lane-keeping and lane-changing intent stage,the characterization index system for predict lane-changing behavior was constructed. The two element Logistic model was introduced to determined the regression coefficient of each parameter and predict the lane-changing behavior. The research result show that the model may predict drivers' lane-changing behavior for at least 2. 5 s in advance,and the prediction accuracy of the start time of the lane-changing behavior is 96. 34%. The accuracy and time series characteristics of the model are superior to the use of turn signals and visual characteristics in predicting lane-changing behaviour.
引文
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2 Butakov V A,Ioannou P.Personalized driver/vehicle lane change models for ADAS[J].IEEE Transactions on Vehicular Technology,2015,64(10):4422-4431
3牛世峰,黎莉.神经网络与贝叶斯滤波器在换道预测中的应用[J].科学技术与工程,2016,16(14):212-216Niu Shifeng,Li Li.The application of bayesian filter and neural networks in lane changing prediction[J].Science Technology and Engineering,2016,16(14):212-216
4 Tu Y,Zhang F,Wang Z.Based on hidden Markov model to identify the driver lane-changing behavior of automobile OBD internet of vehicles research and design[M]//Advances in Computer and Computational Sciences.Singapore:Springer,2018:257-263
5 Nilsson J,Silvlin J,Brannstrom M,et al.If,when,and how to perform lane change maneuvers on highways[J].IEEE Intelligent Transportation Systems Magazine,2016,8(4):68-78
6 Ni J,Liu Z,Tu X,et al.Safety prediction model of lane changing based on driver assistance system[J].Journal of Transportation Systems Engineering and Information Technology,2016,16(4):95-100
7 Peng J,Guo Y,Fu R,et al.Multi-parameter prediction of drivers'lane-changing behaviour with neural network model[J].Applied Ergonomics,2015,50:207-217
8 Zheng J,Suzuki K,Fujita M.Predicting driver's lane-changing decisions using a neural network model[J].Simulation Modelling Practice&Theory,2014,42:73-83
9马勇,付锐,郭应时,等.基于实车试验的驾驶人换道行为多参数预测[J].长安大学学报(自然科学版),2014,34(5):101-108Ma Yong,Fu Rui,Guo Yingshi,et al.Multi-parameter prediction of driver's lane change behavior based on real-world driving tests[J].Journal of Chang'an University(Natural Science),2014,34(5):101-108
10 Peng J,Shao Y.Intelligent method for identifying driving risk based on V2V multisource big data[J/OL].Complexity,2018:1801273https://doi.org/10.1155/2018/1801273
11 Doshi A,Trivedi M M.On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes[J].IEEE Transactions on Intelligent Transportation Systems,2009,10(3):453-462
12徐慧智,裴玉龙,于涛,等.驾驶员车道变换行为视点转移特性研究[J].哈尔滨理工大学学报,2010,15(5):57-60Xu Huizhi,Pei Yulong,Yu Tao,et al.Research on viewpoint transfer characteristics of driver lane change behavior[J].Journal of Harbin University of Science and Technology,2010,15(5):57-60
13 Fitch G M,Lee S E,Klauer S,et al.Analysis of lane-change crashes and near-crashes[J].US Department of Transportation,National Highway Traffic Safety Administration,2009
14 Peng J,Wang C,Shao Y,et al.Visual search efficiency evaluation method for potential connected vehicles on sharp curves[J].IEEEAccess,2018(6):41827-41838
15党睿娜,王建强,李克强,等.高速公路行驶条件下的驾驶员换道特性[J].清华大学学报(自然科学版),2013(10):1481-1485Dang Ruina,Wang Jianqiang,Li Keqiang,et al.Driver lane change characteristics for various highway driving conditions[J].Journal of Tsinghua University(Science and Technology),2013(10):1481-1485