  Current deep neural networks (DNNs) for autonomous driving computer vision
are typically trained on specific datasets that only involve a single type of
data and urban scenes. Consequently, these models struggle to handle new
objects, noise, nighttime conditions, and diverse scenarios, which is essential
for safety-critical applications. Despite ongoing efforts to enhance the
resilience of computer vision DNNs, progress has been sluggish, partly due to
the absence of benchmarks featuring multiple modalities. We introduce a novel
and versatile dataset named InfraParis that supports multiple tasks across
three modalities: RGB, depth, and infrared. We assess various state-of-the-art
baseline techniques, encompassing models for the tasks of semantic
segmentation, object detection, and depth estimation. More visualizations and
the download link for InfraParis are available at
\href{https://ensta-u2is.github.io/infraParis/}{https://ensta-u2is.github.io/infraParis/}.
