Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective
Apostolos Modas, Ricardo Sánchez-Matilla, Pascal Frossard, Andrea Cavallaro
Abstract
Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1]. 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).
Topics & Concepts
Adversarial systemComputer scienceGlobal Positioning SystemSAFERProcess (computing)RadarField (mathematics)Perspective (graphical)LidarDomain (mathematical analysis)Artificial intelligenceHuman–computer interactionReal-time computingComputer securityRemote sensingTelecommunicationsGeographyOperating systemMathematical analysisPure mathematicsMathematicsAdversarial Robustness in Machine LearningBacillus and Francisella bacterial researchPhysical Unclonable Functions (PUFs) and Hardware Security