  With the development of artificial intelligence and breakthroughs in deep
learning, large-scale Foundation Models (FMs), such as GPT, Sora, etc., have
achieved remarkable results in many fields including natural language
processing and computer vision. The application of FMs in autonomous driving
holds considerable promise. For example, they can contribute to enhancing scene
understanding and reasoning. By pre-training on rich linguistic and visual
data, FMs can understand and interpret various elements in a driving scene, and
provide cognitive reasoning to give linguistic and action instructions for
driving decisions and planning. Furthermore, FMs can augment data based on the
understanding of driving scenarios to provide feasible scenes of those rare
occurrences in the long tail distribution that are unlikely to be encountered
during routine driving and data collection. The enhancement can subsequently
lead to improvement in the accuracy and reliability of autonomous driving
systems. Another testament to the potential of FMs' applications lies in World
Models, exemplified by the DREAMER series, which showcases the ability to
comprehend physical laws and dynamics. Learning from massive data under the
paradigm of self-supervised learning, World Model can generate unseen yet
plausible driving environments, facilitating the enhancement in the prediction
of road users' behaviors and the off-line training of driving strategies. In
this paper, we synthesize the applications and future trends of FMs in
autonomous driving. By utilizing the powerful capabilities of FMs, we strive to
tackle the potential issues stemming from the long-tail distribution in
autonomous driving, consequently advancing overall safety in this domain.
