A modular parametric architecture for the TORCS racing engine

Onieva, Enrique; Pelta, David A.; Alonso, Javier; Milanés, Vicente; Pérez, Joshue

doi:10.1109/cig.2009.5286466

Cited by 49 publications

(30 citation statements)

References 7 publications

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“…17 The prime objective of the present work is twofold: First, to design, implement, and test a complete architecture enabling automatic driving in racing situations; and second, to gain insight into how to construct efficient and simple to understand controllers for car bots. In this sense, it is a continuation of the authors' earlier work 18 in which a driving architecture with a fuzzy-based module responsible for calculating the allowed speed achieved excellent results in the 2009 Simulated Car Racing Competition.…”

Section: Introductionmentioning

confidence: 73%

An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver

Onieva¹,

Pelta²,

Godoy³

et al. 2012

Int. J. Intell. Syst.

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This 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. C 2012 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 73%

An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver

Onieva¹,

Pelta²,

Godoy³

et al. 2012

Int. J. Intell. Syst.

Self Cite

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“…The BFB3 follows the philosophy of "modular architecture" proposed by Onieva et al [10]. Figure 1 a) shows the modules making up the controller.…”

Section: Overview Of the Bfb3mentioning

confidence: 99%

“…Onieva et al, the winners of the championships in both 2009 and 2010, proposed a parameterized modular architecture with a fuzzy system and a genetic algorithm [10] [11]. Butz et al developed their own controller named COBOSTAR and won the 2009 competition held by CEC and CIG [12].…”

Section: Introductionmentioning

confidence: 99%

Optimization of an Autonomous Car Controller Using a Self-Adaptive Evolutionary Strategy

Kim

2012

International Journal of Advanced Robotic Systems

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Autonomous cars control the steering wheel, acceleration and the brake pedal, the gears and the clutch using sensory information from multiple sources. Like a human driver, it understands the current situation on the roads from the live streaming of sensory values. The decision-making module often suffers from the limited range of sensors and complexity due to the large number of sensors and actuators. Because it is tedious and difficult to design the controller manually from trial-anderror, it is desirable to use intelligent optimization algorithms. In this work, we propose optimizing the parameters of an autonomous car controller using selfadaptive evolutionary strategies (SAESs) which co-evolve solutions and mutation steps for each parameter. We also describe how the most generalized parameter set can be retrieved from the process of optimization. Open-source car racing simulation software (TORCS) is used to test the goodness of the proposed methods on 6 different tracks. Experimental results show that the SAES is competitive with the manual design of authors and a simple ES.

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“…The inputs are computed and transmitted to driving policies based on hand-coded rules or heuristics that manage steering and throttle controls. Usually, these controllers optimize their driving policies with an evolutionary algorithm [4,18,19]. These kind of controllers are efficient and they have been at the top of the SRC competition since 2009.…”

Section: Introductionmentioning

confidence: 99%