TORCS sensor noise removal and multi-objective track selection for driving style adaptation

Two related problems with TORCS car racing competition controllers are approached here. At first, we demonstrate how to handle the 10% artificial sensor noise that made proper track segment recognition quite difficult for some controllers in 2010 (when the noise was introduced). This is successfully dealt with by a combination of averaging and regression. The presented solution copes well with the natural antagonism between accuracy and the produced time lag, meaning that controllers are enabled to use full sensor information despite noise. Secondly, we suggest a solution for the problem of selecting a minimal set of controller parameter configurations suitable for several different tracks by applying principles of multi-objective optimization. While a full multi-objective approach is unfeasible here, we investigate the conflict potential between objectives (in this case tracks) in order to remove the ones that are less problematic. Naturally, the second problem is much more interesting but can only be tackled if the first one is resolved.