Short-term Vehicle Speed Prediction by Time Series Neural Network in High Altitude Areas

Abstract

Vehicle driving conditions at high altitudes are quite different from those in plain areas. Accurately predicting the speed of vehicles traveling at high altitudes is of great significance for the development of vehicle safety assisted driving. In order to study the vehicles’ speed of highways in high-altitude areas, and predict the speed of vehicles accurately, more than 30 000 data were collected by a medium-sized SUV in Qinghai under typical adverse environmental conditions. The real-time vehicle status data (engine speed, engine torque, transmission gear, throttle opening), road alignment data, and historical vehicle speed data was denoised by wavelet method. The collecting data is time-varying and nonlinear characteristics. A nonlinear auto-regression with exogenous inputs (NARX) dynamic neural network prediction model was established in this paper to fit travel speed. The network model after training has small error and high fitting degree. The accuracy of vehicle speed prediction in the next five seconds is 96.01%. The root mean square error of prediction is less than 2.21 km/h, which can achieve better prediction effect. At the same time, the transplantability of the model is enhanced by taking altitude and road alignment as variables.