  Modern autonomous driving systems rely heavily on deep learning models to
process point cloud sensory data; meanwhile, deep models have been shown to be
susceptible to adversarial attacks with visually imperceptible perturbations.
Despite the fact that this poses a security concern for the self-driving
industry, there has been very little exploration in terms of 3D perception, as
most adversarial attacks have only been applied to 2D flat images. In this
paper, we address this issue and present a method to generate universal 3D
adversarial objects to fool LiDAR detectors. In particular, we demonstrate that
placing an adversarial object on the rooftop of any target vehicle to hide the
vehicle entirely from LiDAR detectors with a success rate of 80%. We report
attack results on a suite of detectors using various input representation of
point clouds. We also conduct a pilot study on adversarial defense using data
augmentation. This is one step closer towards safer self-driving under unseen
conditions from limited training data.
