Pemodelan Dinamika Kendaraan dengan Jaringan Syaraf Tiruan

Authors

  • Satrio Dewanto Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v5i1.2588

Keywords:

autonomous vehicle, artificial neural networks, feedforward neural networks, back propagation method

Abstract

Creating autonomous vehicle that can drive without a driver is a dream of the researchers who see the application of such a system will be needed in the future. Realizing such a system requires a dynamic model of the vehicle. It may be obtained by analytical method using dynamic equations. However, this way is rather difficult to do, especially in modeling the non-linear factors caused by tires, suspension, road conditions, etc. This study, in order to avoid difficulties in the analytical method, used artificial neural networks to model the dynamic system of the autonomous vehicle. It utilized data input from the camera sensor and vehicle speed.
Dimensions

Plum Analytics

References

Ghazizadeh, A., Fahim, M., & El-Gindy. (1996). Neural networks representation of a vehicle model: Neuro-vehicle (NV). Int. J. Veh. Design, 17(1), 55-75.

Mathworks. (n.d.). Modeling a Vehicle Dynamics System. Diakses dari http://www.mathworks.com/help/ident/examples/modeling-a-vehicle-dynamics-system.html

Qiang, L., Huiyi, W., & Konghui, G. (1999, Sept.). Identification and control of fourwheel-steering vehicles based on neural network. Vehicle Electronics Conf., 1, 250-253.

Rivals, I., Canas, D., Personnaz, L., & Dreyfus, G. (1994). Modeling and control of mobile robots and intelligent vehicles by neural networks. IEEE Intelligent Vehicle Symp, June 1994, 137-142.

Yim, Y. U., & Oh, Se-Young. (2004, Jul.). Modeling of Vehicle Dynamics From Real Vehicle Measurements Using a Neural Network With Two-Stage Hybrid Learning for Accurate Long-Term Prediction. IEEE Transactions on Vehicular Technology, 53(4), 1076-1084.

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Published

2014-06-30

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Section

Articles
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