A driver's car-following behavior prediction model based on multi-sensors data

被引:16
|
作者
Wang, Hui [1 ]
Gu, Menglu [1 ]
Wu, Shengbo [1 ]
Wang, Chang [1 ]
机构
[1] Changan Univ, Sch Automobile, Middle Sect, Naner Huan Rd, Xian 710064, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Machine learning; Car following; Sensor data; Prediction model; Time-to-collisionAbbreviations; CAN Controller area network; GMM Gaussian mixed model; GPS Global position system; IPC Industrial personal computer; ROC Receiver operating characteristic curve; SVM Support vector machine; THW Time headway; TTC Time-to-collision; REACTION-TIME; DRIVING BEHAVIOR; SAFETY; VARIABILITY; INTERNET; YOUNGER; DELAY; EDGE; FLOW;
D O I
10.1186/s13638-020-1639-2
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.
引用
收藏
页数:12
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