计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 230-235.doi: 10.11896/jsjkx.190300155
冯安琪, 钱丽萍, 欧阳金源, 吴远
FENG An-qi, QIAN Li-ping, OUYANG Jin-yuan, WU Yuan
摘要: 随着城市化和机动化的快速发展,交通安全越来越受到人们的关注。利用车载网络系统获取车载数据来预测车辆下一时刻的车载状态,对于提高运输路段的交通安全起着重要作用。文中提出一种基于自回归滑动平均(Auto-Regressice Mo-ving Average,ARMA)模型的两级量化自适应卡尔曼滤波算法,来预测车辆的行车状态(行驶的方向、行驶的车道、车辆的速度和加速度)。首先,开发了一个车载网络系统,通过交换车载单元(On-Board Unit,OBU)和路边单元(Roadside Unit,RSU)之间的交通数据来获取车辆数据;然后,通过配置在路边单元的边缘云服务器来预测车辆状态;最后,边缘服务器把预测到的状态信息广播给其他路边单元,以便交叉口其他车辆获取车辆信息。实验结果验证了用于预测加速度的自回归移动平均模型的有效性。此外,文中还评估了所提算法的有效性。与其他3种预测算法相比,所提算法的速度预测精度分别提高了90.62%,89.81%,82.76%,这说明该算法在车载网络中能有效预测车辆状态。
中图分类号:
[1]WHO.Deaths on the roads:based on the WHO global status re-port on road safety 2015[M].World Health Organization (WHO),2015. [2]CHENG N,LYU F,CHEN J Y,et al.Big data driven vehicular networks[J].IEEE Network,2018,32(6):160-167. [3]QIAN L P,WU Y,ZHOU H B,et al.Dynamic cell association for non-orthogonal multiple-access V2S networks[J].IEEE Journal on Selected Areas in Communications,2017,35(10):2342-2356. [4]TAKAHASHI Y,KAWAMOTO Y,NISHIYAMA H,et al.A novel radio resource optimization method for relay-based unmanned aerial vehicles[J].IEEE Transactions on Wireless Communications,2018,17(11):7352-7363. [5]SUN Y L,XU Y,TANG Y L,et al.Traffic offloading for online video service in vehicular networks:a cooperative approach[J].IEEE Transactions on Vehicular Technology,2018,67(8):7630-7642. [6]BHARATI S,ZHUANG W H,THANAYANKIZIL L V,et al.Link-layer cooperation based on distributed TDMA MAC for vehicular networks[J].IEEE Transactions on Vehicular Technology,2017,66(7):6415-6427. [7]LIU K,FENG L,DAI P L,et al.Coding-assisted broadcastscheduling via memetic computing in SDN-based vehicular networks[J].IEEE Transactions on Intelligent Transportation Systems,2018,19(8):2420-2431. [8]RASHDAN L,SCHMIDHAMMER M,MUELLER F P,et al.Performance evaluation of vehicle-to-vehicle communication for cooperative collision avoidance at urban intersections[C]//IEEE 86th Vehicular Technology Conference (VTC-Fall).2017:1-5. [9]HAFEEZ K A,ANPALAGAN A,ZHAO L.Optimizing thecontrol channel interval of the DSRC for vehicular safety applications[J].IEEE Transactions on Vehicular Technology,2016,65(5):3377-3388. [10]KIM B,YI K.Probabilistic and holistic prediction of vehiclestates using sensor fusion for application to integrated vehicle safety systems[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2178-2190. [11]WANG Y,DENG Q X,LIU G H,et al.Dynamic target tracking and predicting algorithm based on combination of motion equation and Kalman filter[J].Computer Science,2015,42(12):76-81. [12]PARK S,KIM B,KANG C,et al.Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture[C]//IEEE Intelligent Vehicles Symposium (IV).2018:1672-1678. [13]FENG A Q,QIAN L P,HUANG Y P,et al.RFID data driven vehicle speed prediction using adaptiveKalman filter [J].Computer Science,2019,46(4):100-105. [14]BASYONI Y,ABBAS H M,TALAAT H,et al,Speed prediction from mobile sensors using cellular phone-based traffic data[J].IET Intelligent Transport Systems,2017,11(7):387-396. [15]HU X,BAO M,ZHANG X,et al.Quantized Kalman filtertracking in directional sensor networks[J].IEEE Transactions on Mobile Computing,2018,17(4):871-883. [16]HU X,BAO M,ZHANG X,et al.Generalized iterated Kalman filter and its performance evaluation[J].IEEE Transactions on Signal Processing,2015,63(12):3204-3217. [17]RAHIMI A,DUNAGAN B,DARRELL T.Tracking peoplewith a sparse network of bearing sensors[C]//Computer Vision(ECCV 2004).Heidelberg:Springer,2004:507-518. [18]WANG X W,SHEN X L,LIU X S.Random error analysis of MEMS gyroscope based on adaptive Kalman filter[J].Chinese Journal of Sensors and Actuators,2017,30(11):1666-1670. [19]LEBRE M,MOUEL F,MENARD E.On the importance of real data for microscopic urban vehicular mobility trace[C]//International Conference on ITS Telecommunications.2015:22-26. [20]LEBRE M,MOUEL F,MENARD E.Partial and local know-ledge for global efficiency of urban vehicular traffic[C]//IEEE 82nd Vehicular Technology Conference.2015:1-5. |
[1] | 于天琪, 胡剑凌, 金炯, 羊箭锋. 基于移动边缘计算的车载CAN网络入侵检测方法 Mobile Edge Computing Based In-vehicle CAN Network Intrusion Detection Method 计算机科学, 2021, 48(1): 34-39. https://doi.org/10.11896/jsjkx.200900181 |
[2] | 樊英, 张达敏, 陈忠云, 王依柔, 徐航, 王栎桥. 基于改进乌鸦算法的车载网络频谱分配方案 Spectrum Allocation Scheme of Vehicular Ad Hoc Networks Based on Improved Crow Search Algorithm 计算机科学, 2020, 47(12): 273-278. https://doi.org/10.11896/jsjkx.190900199 |
[3] | 陈杰, 谢显中, 黄倩, 黎佳. 无线车载网络中一种基于跨层优化的网络编码TCP协议 Network Coding TCP Protocol Based on Cross-layer Optimization in Wireless Vehicle Networks 计算机科学, 2019, 46(2): 88-94. https://doi.org/10.11896/j.issn.1002-137X.2019.02.014 |
[4] | 姚宏,白长敏,胡成玉,曾德泽,梁庆中. 移动数据分流研究综述 Survey on Mobile Data Offloading 计算机科学, 2014, 41(Z11): 182-186. |
[5] | 汪峥,钱焕延,汪婧雅,高德民. 车载物联网中蠕虫传播模型的构建与仿真 Construction and Simulation of Worm Propagation Model in Vehicular Internet of Things 计算机科学, 2012, 39(3): 28-32. |
[6] | 蔡青松,牛建伟,刘畅. 一种基于车载机会网络的自适应数据分发算法 Adaptive Data Dissemination Algorithm for Vehicular Opportunistic Networks 计算机科学, 2011, 38(6): 58-63. |
|