Summary of A Survey on Large Language Model-empowered Autonomous Driving, by Yuxuan Zhu et al.
A Survey on Large Language Model-empowered Autonomous Driving
by Yuxuan Zhu, Shiyi Wang, Wenqing Zhong, Nianchen Shen, Yunqi Li, Siqi Wang, Zhiheng Li, Cathy Wu, Zhengbing He, Li Li
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Artificial intelligence (AI) is crucial for autonomous driving (AD) research, focusing on efficiency and intelligence. Two main approaches are modularization and end-to-end training. Modularization decomposes tasks into separate modules, while end-to-end trains a single model from sensor data to control signals. However, both paths struggle with inconsistent objectives and limited learning capabilities in complex scenarios. Large language models (LLMs) are proposed as a solution, offering powerful reasoning capabilities and extensive knowledge understanding. This paper analyzes the potential applications of LLMs in AD systems, exploring optimization strategies in modular and end-to-end approaches, and discussing the limitations and challenges that LLMs may encounter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is helping develop self-driving cars. There are two main ways to make this happen: breaking down tasks into smaller parts or training one model to do everything. Both methods have their own problems. One solution is to use big language models that can learn and understand lots of information. This paper looks at how these models could be used to improve self-driving car technology, including exploring new ways to train them and discussing the challenges they might face. |
Keywords
* Artificial intelligence * Optimization
