HawkDrive: A Transformer-driven Visual Perception System for Autonomous
  Driving in Night Scene
Ziang Guo Stepan Perminov Mikhail Konenkov Dzmitry Tsetserukou
Many established vision perception systems for autonomous driving scenarios
ignore the influence of light conditions, one of the key elements for driving
safety. To address this problem, we present HawkDrive, a novel perception
system with hardware and software solutions. Hardware that utilizes stereo
vision perception, which has been demonstrated to be a more reliable way of
estimating depth information than monocular vision, is partnered with the edge
computing device Nvidia Jetson Xavier AGX. Our software for low light
enhancement, depth estimation, and semantic segmentation tasks, is a
transformer-based neural network. Our software stack, which enables fast
inference and noise reduction, is packaged into system modules in Robot
Operating System 2 (ROS2). Our experimental results have shown that the
proposed end-to-end system is effective in improving the depth estimation and
semantic segmentation performance. Our dataset and codes will be released at
https://github.com/ZionGo6/HawkDrive.