  Current end-to-end deep learning driving models have two problems: (1) Poor
generalization ability of unobserved driving environment when diversity of
training driving dataset is limited (2) Lack of accident explanation ability
when driving models don't work as expected. To tackle these two problems,
rooted on the believe that knowledge of associated easy task is benificial for
addressing difficult task, we proposed a new driving model which is composed of
perception module for \textit{see and think} and driving module for
\textit{behave}, and trained it with multi-task perception-related basic
knowledge and driving knowledge stepwisely.
  Specifically segmentation map and depth map (pixel level understanding of
images) were considered as \textit{what \& where} and \textit{how far}
knowledge for tackling easier driving-related perception problems before
generating final control commands for difficult driving task. The results of
experiments demonstrated the effectiveness of multi-task perception knowledge
for better generalization and accident explanation ability. With our method the
average sucess rate of finishing most difficult navigation tasks in untrained
city of CoRL test surpassed current benchmark method for 15 percent in trained
weather and 20 percent in untrained weathers. Demonstration video link is:
https://www.youtube.com/watch?v=N7ePnnZZwdE
