Abstract
A simulation environment is presented in this paper for the autonomous driving of a car, along with its respective obstacles, tracks and tests. Unity 3D, which is a cross-platform game development platform and engine, powers this environment by providing basic navigation controls along with the functions for creating and recording the car’s parameters as an input dataset. Using a convolutional neural network, the system predicts the future output, thus achieving complete autonomous navigation, compatible with any environment.
The paper also compares various pre-processing methods used on the input data, so as to find the most efficient model and to study which method contributes the most to effective autonomous driving.
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Dube, B., Kazi, R., Malya, A., Joshi, M. (2020). Simulation of a Self-Driving Car and Comparison of Various Training Methods. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_44
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DOI: https://doi.org/10.1007/978-3-030-30465-2_44
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