@InProceedings{FelipeCodevilla2018,
author="Felipe Codevilla
and Antonio Lopez
and Vladlen Koltun
and Alexey Dosovitskiy",
title="On Offline Evaluation of Vision-based Driving Models",
booktitle="15th European Conference on Computer Vision",
year="2018",
volume="11219",
pages="246--262",
optkeywords="Autonomous driving",
optkeywords="deep learning",
abstract="Autonomous driving models should ideally be evaluated by deployingthem on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset andsuitable offline metrics.",
optnote="ADAS; 600.124; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3162), last updated on Mon, 24 Jan 2022 13:07:08 +0100",
opturl="https://doi.org/10.1007/978-3-030-01267-0_15",
file=":http://refbase.cvc.uab.es/files/CLK2018.pdf:PDF"
}