Task: Summarization
Input (English): Current end-to-end deep learning driving models have two problems: (1) Poor
 generalization ability of unobserved driving environment when diversity of train-
 ing 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 be-
 lieve 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 see and think and driving module for behave, and trained it with multi-task
 perception-related basic knowledge and driving knowledge stepwisely.  Specifi-
 cally segmentation map and depth map (pixel level understanding of images) were
 considered as what & where and 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 abil-
 ity. 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.
Output (English): Presents a multitask learning architecture for depth and segmentation map estimation and the driving prediction using a perception module and a driving decision module.