In recent years, there has been an increasing interest in exploiting readily available background knowledge in order to obtain neural models (i) able to learn from less data, and/or (ii) guaranteed to be compliant with the background knowledge corresponding to requirements about the model. In this challenge, we focus on the autonomous driving domain, and we provide our participants with the recently proposed ROAD-R dataset, which consists of 22 long videos annotated with road events together with a set of requirements expressing well known facts about the world (e.g., “a traffic light cannot be red and green at the same time”). The participants will face two challenging tasks. In the first, they will have to develop the best performing model with only a subset of the annotated data, which in turn will encourage them to exploit the requirements to facilitate training on the unlabelled portion of the dataset. In the second, we ask them to create systems whose predictions are compliant with the requirements. This is the first competition addressing the open questions: (i) If limited annotated data is available, is background knowledge useful to obtain good performance? If so, how can it be injected in deep learning models? And, (ii) how can we design effective deep learning based systems that are compliant with a set of requirements? As a consequence, this challenge is expected to bring together people from different communities, especially those interested in the general topic of Safe-AI as well as in the autonomous driving application domain, and also researchers working in the neuro-symbolic AI, semi-supervised learning and action recognition.