Automatic vehicle route prediction based on multi-sensor fusion

被引：0

作者：

Xu H. ^{[1
,2
]}

Feng B. ^{[2
]}

Peng Y. ^{[3
]}

机构：

[1] Integrated Circuit Design and Embedded Application Research Center, Chizhou University, Chizhou

[2] Institute of Advanced Manufacturing Tech., Chinese Academy of Sci., Changzhou

[3] Dept. of Engg. Tech., Chizhou Huayu Electronic Tech. Co. Ltd., Chizhou

来源：

International Journal of Vehicle Structures and Systems | 2021年 / 13卷 / 04期

关键词：

Automatic prediction; Carsim; Fusion; Multi-sensor; Vehicle route;

D O I：

10.4273/ijvss.13.4.13

中图分类号：

学科分类号：

摘要：

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction. © 2021.

引用

页码：457 / 463

页数：6

共 20 条

[1]

Lu Z.J., Liu X., Qin Y.G., An adaptive multi-sensor management method for cooperative perception, J. China Academy of Elec. & Info. Tech, 12, 4, pp. 353-358, (2017)

[2]

Chai X.H., Zhang C.J., Xiao H.W., One improved EMF harmonic adaptive compensation for PMSG sensorless control method, J. Power Supply, 15, 1, pp. 55-61, (2017)

[3]

Wang N.S., Li X.F., Fang C., Design of contactless power and data transmission system for buoy's underwater sensors, Chinese J. Power Sources, 41, 1, pp. 131-133, (2017)

[4]

Li D.L., Chen H.Z., Zhang G., Design of traffic light timing control system based on temperature and humidity sensor, Automation& Instrument, 7, pp. 82-83, (2017)

[5]

Lou G.H., Zhang J.P., Ranging distance modified by particle swarm algorithm for WSN node localization, J. Jilin University (Sci. Ed.), 56, 3, pp. 188-194, (2018)

[6]

Zhang J., Simulation optimization of sensitive data transmission efficiency in wireless sensor networks, Computer Simulation, 34, 10, pp. 277-280, (2017)

[7]

Suhr J.K., Jung H.G., Sensor fusion-based precise obstacle localisation for automatic parking systems, Electronics Letters, 54, 7, pp. 445-447, (2018)

[8]

Rozsa Z., Sziranyi T., Obstacle prediction for automated guided vehicles based on point clouds measured by a tilted LIDAR sensor, IEEE Trans. Intelligent Transportation Systems, PP, 99, pp. 1-13, (2018)

[9]

Guo L., Manglani S., Liu Y., Jia Y., Automatic sensor correction of autonomous vehicles by Human-Vehicle teaching-and-learning, IEEE Trans. Vehicular Tech, 67, 9, pp. 8085-8099, (2018)

[10]

Li H., Li X., Ding W., Ding W., Li Y., Multi-sensor based high-precision direct georeferencing of medium-altitude unmanned aerial vehicle images, Int. J. Remote Sensing, 38, 8-10, pp. 2577-2602, (2017)

← 1 2 →