Abstract
This work presents a comparison between different Convolutional Neural Network models, testing its performance when it leads a self-driving car in a simulated environment. To do so, driving data has been obtained manually driving the simulator as ground truth and different network models with diverse complexity levels has been created and trained with the data previously obtained using end-to-end deep learning techniques. Once this CNNs are trained, they are tested in the driving simulator, checking their ability of minimizing the car distance to the center of the lane, its heading error and its RMSE. The neural networks will be evaluated according to these parameters. Finally, conclusions will be drawn about the performance of the different models according to the parameters mentioned before in order to find the optimum CNN for the developed application.
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Acknowledgment
This work has been partially funded by the Spanish MINECO/FEDER through the SmartElderlyCar project (TRA2015-70501-C2-1-R), the DGT through the SERMON project (SPIP2017-02305), and from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds.
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del Egio, J., Bergasa, L.M., Romera, E., Gómez Huélamo, C., Araluce, J., Barea, R. (2019). Self-driving a Car in Simulation Through a CNN. In: Fuentetaja Pizán, R., García Olaya, Á., Sesmero Lorente, M., Iglesias Martínez, J., Ledezma Espino, A. (eds) Advances in Physical Agents. WAF 2018. Advances in Intelligent Systems and Computing, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-99885-5_3
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DOI: https://doi.org/10.1007/978-3-319-99885-5_3
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