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The emergence of data-driven machine learning (ML) has facilitatedsignificant progress in many complicated tasks such as highly-automateddriving. While much effort is put into improving the ML models and learningalgorithms in such applications, little focus is put into how the training dataand/or validation setting should be designed. In this paper we investigate theinfluence of several data design choices regarding training and validation ofdeep driving models trainable in an end-to-end fashion. Specifically, (i) weinvestigate how the amount of training data influences the final drivingperformance, and which performance limitations are induced through currentlyused mechanisms to generate training data. (ii) Further, we show by correlationanalysis, which validation design enables the driving performance measuredduring validation to generalize well to unknown test environments. (iii)Finally, we investigate the effect of random seeding and non-determinism,giving insights which reported improvements can be deemed significant. Ourevaluations using the popular CARLA simulator provide recommendations regardingdata generation and driving route selection for an efficient future developmentof end-to-end driving models.

On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models