  Recent years have witnessed much interest in expanding the use of networking
signals beyond communication to sensing, localization, robotics, and autonomous
systems. This paper explores how we can leverage recent advances in 5G
millimeter wave (mmWave) technology for imaging in self-driving cars.
Specifically, the use of mmWave in 5G has led to the creation of compact phased
arrays with hundreds of antenna elements that can be electronically steered.
Such phased arrays can expand the use of mmWave beyond vehicular communications
and simple ranging sensors to a full-fledged imaging system that enables
self-driving cars to see through fog, smog, snow, etc. Unfortunately, using
mmWave signals for imaging in self-driving cars is challenging due to the very
low resolution, the presence of fake artifacts resulting from multipath
reflections and the absence of portions of the car due to specularity. This
paper presents HawkEye, a system that can enable high resolution mmWave imaging
in self driving cars. HawkEye addresses the above challenges by leveraging
recent advances in deep learning known as Generative Adversarial Networks
(GANs). HawkEye introduces a GAN architecture that is customized to mmWave
imaging and builds a system that can significantly enhance the quality of
mmWave images for self-driving cars.
