DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder

Automated vehicles are a fundamental component of intelligent transportation systems and will improve safety, traffic efficiency and driving experience, and reduce human errors [1]. Deep learning solutions are used in several autonomous vehicle subsystems in order to perform perception, sensor fusion, scene analysis, and path planning (e.g., [2, 3, 4, 5, 6]). State-of-the-art and human-competitive performance have been achieved by such algorithms on many computer vision tasks related to autonomous vehicles [7]. Nevertheless, over the last years it was demonstrated that deep-learning-based solutions are susceptible to various threats and vulnerabilities that can cause the autonomous vehicles to misbehave in unexpected and potentially dangerous ways. Besides physical attacks that can induce erroneous behaviour there is also the possibility that the camera data can be manipulated directly thus eliciting false algorithmic inferences. This can cause an AI-based perception module/controller to make incorrect decisions, such as when an autonomous vehicle fails to detect a lane/ parking marking and results in a collision [8, 9]. An example of such an attack can be either data injection by malicious software installation [10] in the CAN bus, projecting patterns [11], manipulation of over the air updates [12, 13], or even faults [14]. Hence crafting appropriate defenses against attacks that go beyond the hardware layer is important to realize safe autonomous driving [9, 15, 8].