Authors: Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger, Andreas Geiger, Kashyap Chitta
Abstract: End-to-end driving systems have made rapid progress, but have so far not been
applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a
large body of literature on end-to-end architectures and training strategies,
the impact of the training dataset is often overlooked. In this work, we make a
first attempt at end-to-end driving for Leaderboard 2.0. Instead of
investigating architectures, we systematically analyze the training dataset,
leading to new insights: (1) Expert style significantly affects downstream
policy performance. (2) In complex data sets, the frames should not be weighted
on the basis of simplistic criteria such as class frequencies. (3) Instead,
estimating whether a frame changes the target labels compared to previous
frames can reduce the size of the dataset without removing important
information. By incorporating these findings, our model ranks first and second
respectively on the map and sensors tracks of the 2024 CARLA Challenge, and
sets a new state-of-the-art on the Bench2Drive test routes. Finally, we uncover
a design flaw in the current evaluation metrics and propose a modification for
future challenges. Our dataset, code, and pre-trained models are publicly
available at https://github.com/autonomousvision/carla_garage.
Source: http://arxiv.org/abs/2412.09602v1
