Abstract
When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.
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Acknowledgements
This work has been supported in part by projects EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministerio de Economy Competitividad), PROY-PP2015-06 (Plan Propio 2015 UGR), PETRA (SPIP2014-01437, funded by Dirección General de Tráfico), CEI2015-MP-V17 (awarded by CEI BioTIC Granada), and PRY142/14 (funded by Fundación Pública Andaluza Centro de Estudios Andaluces en la IX Convocatoria de Proyectos de Investigación).
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Salem, M., Mora, A.M., Merelo, J.J., García-Sánchez, P. (2017). Driving in TORCS Using Modular Fuzzy Controllers. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_24
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DOI: https://doi.org/10.1007/978-3-319-55849-3_24
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