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

Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather