Liu Jun, Han Jiuqiang, Lv Hongqiang, Li Bing
School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2015 Apr 16;15(4):9000-21. doi: 10.3390/s150409000.
With the continuing growth of highway construction and vehicle use expansion all over the world, highway vehicle traffic rule violation (TRV) detection has become more and more important so as to avoid traffic accidents and injuries in intelligent transportation systems (ITS) and vehicular ad hoc networks (VANETs). Since very few works have contributed to solve the TRV detection problem by moving vehicle measurements and surveillance devices, this paper develops a novel parallel ultrasonic sensor system that can be used to identify the TRV behavior of a host vehicle in real-time. Then a two-dimensional state method is proposed, utilizing the spacial state and time sequential states from the data of two parallel ultrasonic sensors to detect and count the highway vehicle violations. Finally, the theoretical TRV identification probability is analyzed, and actual experiments are conducted on different highway segments with various driving speeds, which indicates that the identification accuracy of the proposed method can reach about 90.97%.
随着全球高速公路建设的持续发展和车辆使用的不断增加，高速公路车辆交通违规（TRV）检测在智能交通系统（ITS）和车载自组织网络（VANET）中对于避免交通事故和人员伤亡变得越来越重要。由于通过移动车辆测量和监测设备来解决TRV检测问题的相关研究很少，本文开发了一种新型并行超声传感器系统，可用于实时识别主车辆的TRV行为。然后提出了一种二维状态方法，利用两个并行超声传感器数据中的空间状态和时间序列状态来检测和统计高速公路车辆违规情况。最后，分析了理论TRV识别概率，并在不同高速公路路段以不同行驶速度进行了实际实验，结果表明所提方法的识别准确率可达约90.97%。
