However, VLMs face challenges in real-world deployment, including processing high-dimensional video streams in real time and managing inference latency. Current models like DriveVLM exhibit delays that are unacceptable for critical driving situations. Despite these hurdles, Zhang is optimistic that advancements in model distillation and edge computing will make VLMs more efficient, enabling real-time processing and on-the-fly decision-making in autonomous vehicles. This could lead to a new era of AVs that better navigate the complexities of the real world.
Key takeaways:
- The autonomous vehicle industry faces challenges with the "long tail problem," where AVs struggle with rare, unforeseen scenarios.
- Vision-Language Models (VLMs) offer a new approach by integrating computer vision and natural language processing to improve AVs' understanding of complex environments.
- End-to-end VLM architectures, like Waymo's EMMA, unify perception and planning, reducing errors and enhancing decision-making through self-supervised learning and chain-of-thought reasoning.
- Real-world deployment of VLMs faces challenges such as processing high-dimensional video streams in real time and reducing inference latency, but future advancements in model distillation and edge computing may address these issues.