Autonomous Vehicle Tracking Using Two TDNN Neural Networks


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 3, No. 5, pp. 1037-1045, Sep. 1996
10.3745/KIPSTE.1996.3.5.1037,   PDF Download:

Abstract

In this paper, the parallel model for stereo camera is employed to find the heading angle and the distance between a leading vehicle and the following vehicle, BART(Binocular Autonomous Resaerch Team vehicle). Two TDNNs(Time Delay Network) such ac S-TDNN and A-TDNN are introduced to control BART. S-TDNN controls the speed of the following vehicle while A-TDNN controls the steering angle of BART. A human drives BART to collect data which are user for training the said neural networks. The trained networks performed the vehicle tracking function satisfactorily under the same driving conditions performed by the human driver. The neural network approach has good portability which decreases costs and saves development time for the different types of vehicles.


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Cite this article
[IEEE Style]
L. H. Man, "Autonomous Vehicle Tracking Using Two TDNN Neural Networks," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 3, no. 5, pp. 1037-1045, 1996. DOI: 10.3745/KIPSTE.1996.3.5.1037.

[ACM Style]
Lee Hee Man. 1996. Autonomous Vehicle Tracking Using Two TDNN Neural Networks. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 3, 5, (1996), 1037-1045. DOI: 10.3745/KIPSTE.1996.3.5.1037.