Browsing by Author "Onieva, E."
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Item Automatic lateral control for unmanned vehicles via genetic algorithms(2011-01) Onieva, E.; Naranjo, J. E.; Milanés, V.; Alonso, J.; García, R.; Pérez, J.; CCAMIt is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmanned control of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative genetic algorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.Item Erratum: Location-based crowdsourcing for vehicular communication in hybrid networks (IEEE Transactions on Intelligent Transportation Systems (2013) 14:2 (837-846))(2013) Milanes, V.; Villagra, J.; Godoy, J.; Simo, J.; Pérez, J.; Onieva, E.; CCAMItem An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver(2012-03) Onieva, E.; Pelta, D. A.; Godoy, J.; Milanés, V.; Pérez, J.; CCAMThis work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels.