  Visual perception in autonomous driving is a crucial part of a vehicle to
navigate safely and sustainably in different traffic conditions. However, in
bad weather such as heavy rain and haze, the performance of visual perception
is greatly affected by several degrading effects. Recently, deep learning-based
perception methods have addressed multiple degrading effects to reflect
real-world bad weather cases but have shown limited success due to 1) high
computational costs for deployment on mobile devices and 2) poor relevance
between image enhancement and visual perception in terms of the model ability.
To solve these issues, we propose a task-driven image enhancement network
connected to the high-level vision task, which takes in an image corrupted by
bad weather as input. Specifically, we introduce a novel low memory network to
reduce most of the layer connections of dense blocks for less memory and
computational cost while maintaining high performance. We also introduce a new
task-driven training strategy to robustly guide the high-level task model
suitable for both high-quality restoration of images and highly accurate
perception. Experiment results demonstrate that the proposed method improves
the performance among lane and 2D object detection, and depth estimation
largely under adverse weather in terms of both low memory and accuracy.
