  Neuromorphic event cameras are useful for dynamic vision problems under
difficult lighting conditions. To enable studies of using event cameras in
automobile driving applications, this paper reports a new end-to-end driving
dataset called DDD20. The dataset was captured with a DAVIS camera that
concurrently streams both dynamic vision sensor (DVS) brightness change events
and active pixel sensor (APS) intensity frames. DDD20 is the longest event
camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera
and vehicle human control data collected from 4000km of highway and urban
driving under a variety of lighting conditions. Using DDD20, we report the
first study of fusing brightness change events and intensity frame data using a
deep learning approach to predict the instantaneous human steering wheel angle.
Over all day and night conditions, the explained variance for human steering
prediction from a Resnet-32 is significantly better from the fused DVS+APS
frames (0.88) than using either DVS (0.67) or APS (0.77) data alone.
