  All-day and all-weather navigation is a critical capability for autonomous
driving, which requires proper reaction to varied environmental conditions and
complex agent behaviors. Recently, with the rise of deep learning, end-to-end
control for autonomous vehicles has been well studied. However, most works are
solely based on visual information, which can be degraded by challenging
illumination conditions such as dim light or total darkness. In addition, they
usually generate and apply deterministic control commands without considering
the uncertainties in the future. In this paper, based on imitation learning, we
propose a probabilistic driving model with ultiperception capability utilizing
the information from the camera, lidar and radar. We further evaluate its
driving performance online on our new driving benchmark, which includes various
environmental conditions (e.g., urban and rural areas, traffic densities,
weather and times of the day) and dynamic obstacles (e.g., vehicles,
pedestrians, motorcyclists and bicyclists). The results suggest that our
proposed model outperforms baselines and achieves excellent generalization
performance in unseen environments with heavy traffic and extreme weather.
