  Traditional methods for autonomous driving are implemented with many building
blocks from perception, planning and control, making them difficult to
generalize to varied scenarios due to complex assumptions and
interdependencies. Recently, the end-to-end driving method has emerged, which
performs well and generalizes to new environments by directly learning from
export-provided data. However, many existing methods on this topic neglect to
check the confidence of the driving actions and the ability to recover from
driving mistakes. In this paper, we develop an uncertainty-aware end-to-end
trajectory generation method based on imitation learning. It can extract
spatiotemporal features from the front-view camera images for scene
understanding, and then generate collision-free trajectories several seconds
into the future. The experimental results suggest that under various weather
and lighting conditions, our network can reliably generate trajectories in
different urban environments, such as turning at intersections and slowing down
for collision avoidance. Furthermore, closed-loop driving tests suggest that
the proposed method achieves better cross-scene/platform driving results than
the state-of-the-art (SOTA) end-to-end control method, where our model can
recover from off-center and off-orientation errors and capture 80% of dangerous
cases with high uncertainty estimations.
