  Once an academic venture, autonomous driving has received unparalleled
corporate funding in the last decade. Still, the operating conditions of
current autonomous cars are mostly restricted to ideal scenarios. This means
that driving in challenging illumination conditions such as night, sunrise, and
sunset remains an open problem. In these cases, standard cameras are being
pushed to their limits in terms of low light and high dynamic range
performance. To address these challenges, we propose, DSEC, a new dataset that
contains such demanding illumination conditions and provides a rich set of
sensory data. DSEC offers data from a wide-baseline stereo setup of two color
frame cameras and two high-resolution monochrome event cameras. In addition, we
collect lidar data and RTK GPS measurements, both hardware synchronized with
all camera data. One of the distinctive features of this dataset is the
inclusion of high-resolution event cameras. Event cameras have received
increasing attention for their high temporal resolution and high dynamic range
performance. However, due to their novelty, event camera datasets in driving
scenarios are rare. This work presents the first high-resolution, large-scale
stereo dataset with event cameras. The dataset contains 53 sequences collected
by driving in a variety of illumination conditions and provides ground truth
disparity for the development and evaluation of event-based stereo algorithms.
