  Autonomous vehicles increasingly rely on cameras to provide the input for
perception and scene understanding and the ability of these models to classify
their environment and objects, under adverse conditions and image noise is
crucial. When the input is, either unintentionally or through targeted attacks,
deteriorated, the reliability of autonomous vehicle is compromised. In order to
mitigate such phenomena, we propose DriveGuard, a lightweight spatio-temporal
autoencoder, as a solution to robustify the image segmentation process for
autonomous vehicles. By first processing camera images with DriveGuard, we
offer a more universal solution than having to re-train each perception model
with noisy input. We explore the space of different autoencoder architectures
and evaluate them on a diverse dataset created with real and synthetic images
demonstrating that by exploiting spatio-temporal information combined with
multi-component loss we significantly increase robustness against adverse image
effects reaching within 5-6% of that of the original model on clean images.
