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Driving as a human: a track learning based adaptable architecture for a car racing controller

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Abstract

We present the evolution and current state of the Mr. Racer car racing controller that excelled at the corresponding TORCS competitions of the last years. Although several heuristics and black-box optimization methods are employed, the basic idea of the controller architecture has been to take over many of the mechanisms human racing drivers apply. They learn the track geometry, plan ahead, and wherever necessary, adapt their plans to the current circumstances quickly. Mr. Racer consists of several modules that have partly been adapted and optimized separately, where the final tuning is usually done with respect to a certain racing track during the warmup phase of the TORCS competitions. We also undertake an experimental evaluation that investigates how the controller profits from adding some of the modules to a basic configuration and which modules are most important for reaching the best possible performance.

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Notes

  1. See the tutorial [15] for a current description, as details have been changed meanwhile.

  2. http://cig.dei.polimi.it/?page_id=175.

  3. http://www.berniw.org/tutorials/robot/tutorial.html.

  4. This can only happen at the start/finish line. For the rest of the track, a straight segment in the track model is always prepended and followed by a corner.

  5. provided in German, translated into English: “Modelling the steering behavior of a vehicle in a car racing simulation”.

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Authors and Affiliations

  1. Chair of Algorithm Engineering, Computational Intelligence Group, Department of Computer Science, Technische Universität Dortmund, Dortmund, Germany

    Jan Quadflieg & Günter Rudolph

  2. European Research Center for Information Systems (ERCIS), WWU Münster, Münster, Germany

    Mike Preuss

Authors
  1. Jan Quadflieg
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  2. Mike Preuss
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  3. Günter Rudolph
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Correspondence to Jan Quadflieg.

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Quadflieg, J., Preuss, M. & Rudolph, G. Driving as a human: a track learning based adaptable architecture for a car racing controller. Genet Program Evolvable Mach 15, 433–476 (2014). https://doi.org/10.1007/s10710-014-9227-z

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  • DOI: https://doi.org/10.1007/s10710-014-9227-z

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