Summary of Tokenize the World Into Object-level Knowledge to Address Long-tail Events in Autonomous Driving, by Ran Tian et al.
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
by Ran Tian, Boyi Li, Xinshuo Weng, Yuxiao Chen, Edward Schmerling, Yue Wang, Boris Ivanovic, Marco Pavone
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TOKEN model is a novel Multi-Modal Large Language Model that tokenizes the world into object-level knowledge to enhance autonomous vehicle planning in long-tail scenarios. It alleviates data scarcity and inefficient tokenization by leveraging traditional end-to-end driving models to produce condensed and semantically enriched representations of the scene, optimized for LLM planning compatibility through deliberate representation and reasoning alignment training stages. The results demonstrate that TOKEN excels in grounding, reasoning, and planning capabilities, outperforming existing frameworks with a 27% reduction in trajectory L2 error and a 39% decrease in collision rates in long-tail scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TOKEN is a new way for autonomous vehicles to understand the world and make good decisions. It takes the big picture of what’s happening on the road and breaks it down into smaller pieces, like objects and events. This helps the vehicle plan better routes and avoid accidents. The old way of doing things wasn’t working well, especially in rare situations. TOKEN solves this problem by using a special kind of model that’s good at understanding language. It also works with other models to make sure everything is aligned correctly. |
Keywords
» Artificial intelligence » Alignment » Grounding » Large language model » Multi modal » Token » Tokenization
