Computer Science ›› 2020, Vol. 47 ›› Issue (5): 230-235.doi: 10.11896/jsjkx.190300155

• Artificial Intelligence • Previous Articles     Next Articles

Vehicular Networking Enabled Vehicle State Prediction with Two-level Quantized AdaptiveKalman Filtering

FENG An-qi, QIAN Li-ping, OUYANG Jin-yuan, WU Yuan   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2019-03-29 Online:2020-05-15 Published:2020-05-19
  • About author:FENG An-qi,born in 1995,postgra-duate.Her main research interests include network and intelligent systems,vehicular networking and Internet of Things technologies.
    QIAN Li-ping,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include wireless communication network,vehicular networking and Internet of Things technologies.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61379122) and Natural Science Foundation of Zhejiang Province,China (LR16F010003)

Abstract: With the rapid development of urbanization and motorization,traffic safety issues have been drawing more and more attentions.The accurate prediction of vehicle state based on the data acquired by the vehicular networking system plays an important role in improving the traffic safety in transportation section.This paper proposes a two-level quantized adaptive Kalman filter algorithm (QAKF) based on the auto-regressive moving average (ARMA) model,to predict the vehicle state (i.e.,the moving direction,driving lane,vehicle speed,and acceleration).First of all,a vehicular networking system is developed to acquire the vehicle data by exchanging traffic data among the on-board unit (OBU) and the roadside unit (RST).Then,the vehicle state is predicted at the edge cloud server equipped at the roadside unit.Finally,the edge cloud server broadcasts the predicted state to other roadside units for other vehicles at the intersection to obtain vehicle information.The numerical results verify the effectiveness of the auto-regressive moving average model used for predicting acceleration.And,this paper evaluates the efficiency of the proposed algorithm.Compared with the other three prediction algorithms,the proposed algorithm can improve the speed prediction accuracy by 90.62%,89.81% and 82.76%,respectively,which implies that this algorithm can effectively predict the vehicle state in vehicular networks.

Key words: Auto-regressive moving average model, Quantized adaptive Kalman filter, Traffic safety, Vehicle state predication, Vehicular networks

CLC Number: 

  • TP391
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