  In this paper, we present a proof of concept for adversarially attacking the
image-based localization module of an autonomous vehicle. This attack aims to
cause the vehicle to perform a wrong navigational decisions and prevent it from
reaching a desired predefined destination in a simulated urban environment. A
database of rendered images allowed us to train a deep neural network that
performs a localization task and implement, develop and assess the adversarial
pattern. Our tests show that using this adversarial attack we can prevent the
vehicle from turning at a given intersection. This is done by manipulating the
vehicle's navigational module to falsely estimate its current position and thus
fail to initialize the turning procedure until the vehicle misses the last
opportunity to perform a safe turn in a given intersection.
