  Autonomous Vehicles rely on accurate and robust sensor observations for
safety critical decision-making in a variety of conditions. Fundamental
building blocks of such systems are sensors and classifiers that process
ultrasound, RADAR, GPS, LiDAR and camera signals~\cite{Khan2018}. It is of
primary importance that the resulting decisions are robust to perturbations,
which can take the form of different types of nuisances and data
transformations, and can even be adversarial perturbations (APs). Adversarial
perturbations are purposefully crafted alterations of the environment or of the
sensory measurements, with the objective of attacking and defeating the
autonomous systems. A careful evaluation of the vulnerabilities of their
sensing system(s) is necessary in order to build and deploy safer systems in
the fast-evolving domain of AVs. To this end, we survey the emerging field of
sensing in adversarial settings: after reviewing adversarial attacks on sensing
modalities for autonomous systems, we discuss countermeasures and present
future research directions.
