Publication

Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Pascal Frossard, Andrea Cavallaro, Apostolos Modas
2020
Journal Articles

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).

Official source

https://infoscience.epfl.ch/entities/publication/41d89e9d-de1a-425c-8172-74e61fc95406

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Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

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