Sensorless but not Senseless: Prediction in Evolutionary Car Racing

In this paper we try to develop predictors in order to drive a simulated car around a track without the most recent sensor data. In order to test the predictive abilities of our car we developed two experiments: one where the sensor data was interrupted for a certain time and another where the sensor data is constantly delayed by a certain amount. The predictors are based on neural networks, and we compare backpropagation and evolutionary computation as methods of training these. In the end we found that predictors with good driving performance do not sample the set of predictors which minimize the prediction error in the sensors