With the rapid development of artificial intelligence and deep learning, large-scale Foundation Models (FMs) such as GPT and CLIP have shown remarkable achievements in various fields, including natural language processing and computer vision. The potential application of FMs in autonomous driving is an exciting prospect. FMs can play a significant role in enhancing scene understanding and reasoning in autonomous vehicles.
By pre-training on extensive linguistic and visual data, FMs can develop a deep understanding of the various elements present in a driving scene. This understanding allows FMs to interpret the scene and provide cognitive reasoning, enabling them to give linguistic commands and action plans for driving decisions and planning. This capability can greatly enhance the accuracy and reliability of autonomous driving systems.
One particularly intriguing aspect of FMs in autonomous driving is their ability to augment data based on their understanding of driving scenarios. FMs can generate feasible scenes of rare occurrences that may not be encountered during routine driving and data collection. This enhancement can allow autonomous driving systems to better handle the long-tail distribution – situations that occur infrequently but are still critical for safe driving.
The development of World Models, such as the DREAMER series, further demonstrates the potential of FMs in autonomous driving. World Models leverage massive amounts of data and self-supervised learning to comprehend physical laws and dynamics. By generating unseen yet plausible driving environments, World Models can contribute to improved predictions of road user behavior and the offline training of driving strategies.
In summary, the applications of FMs in autonomous driving are vast and promising. By harnessing the powerful capabilities of FMs, we can address potential challenges arising from the long-tail distribution in autonomous driving and significantly advance overall safety in this domain.