Terrain Classification Research Paper
Terrain Classification in Automobiles using Artificial Neural Networks (ANN)
Kodi Balasriram, Tanmay Pravin Pawar, Manimozhi M
Abstract— The tire and road surface interaction are important to study vehicle dynamics as one of the key determinants in assessing the performance of vehicle handling, ride quality, and stability. An active suspension model is used to generate the vibrations caused in real time between the vehicle and the road surface. The data acquired from the active suspension model are terrain profile data and respective vibration data. A learning vector quantization (LVQ) neural network architecture has
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I. Introduction
The friction coefficient between the road and the tire for various road environments can lead to loss of stability and control over the vehicle; if not managed properly. In terrain classification computes various challenges regarding road surface interaction with the moving vehicle body with suspension. This will not only increase the ease of driving a vehicle for different terrains but also will reduce the human errors caused by the driver for different terrains. Terrains can be classified depending upon the type of road surfaces such as asphalt, grass, muddy and rock-bound. These terrains are being defined based upon the active suspension mechanism which comprises the complete vehicle modelling between the car body, its suspension and the tire of the vehicle with respect to the road surface. Using the vibration frequency generated between the vehicle and the road surface, neural network approach is
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results and discussion
A. Active suspension results By comparing the Simulink model with the practical results which are acquired from Active suspension model, it is observed that the settling time for vibrations generated in tire, interacting with road surface and the vehicle chassis is more as seen in the Fig:8. Also, a small amount of tolerance is also present. There is an existence of noise in real time active suspension model.
Figure 8: Comparison of real time model with Simulink
B. Validating terrains with signals Different types of signals like sinusoidal, saw tooth, square wave have been related with different types of road surfaces. Each signal has been studied for the respective road surface with different amplitudes and frequency. The validation of signals with real time surfaces is been achieved. Thus, to classify different types of terrains, the system has been designed based upon the vibrations feedback. And classifying them into asphalt, grassy, muddy and rock-bound. As observed, the scaled conjugate back propagation algorithm successfully identified the surfaces for different
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