Summary of Large Language Models For Human-like Autonomous Driving: a Survey, by Yun Li et al.
Large Language Models for Human-like Autonomous Driving: A Survey
by Yun Li, Kai Katsumata, Ehsan Javanmardi, Manabu Tsukada
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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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 The paper explores the integration of Large Language Models (LLMs) into Autonomous Driving (AD) systems. LLMs have revolutionized the field of AD by enabling learning-based techniques like deep reinforcement learning. The authors highlight key advancements in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end-to-end AD systems. They identify pressing challenges and propose promising research directions to bridge the gap between LLMs and AD. The survey covers the basics of LLMs’ features and training schemes, as well as their applications in modular and end-to-end AD systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about really smart computers called Large Language Models (LLMs) that are helping self-driving cars get better at driving like humans do. These models can understand language and generate text, which is helpful for autonomous vehicles to make decisions on the road. The authors look at how these LLMs can be used in different parts of self-driving car systems and highlight some challenges they need to overcome. They also suggest new areas of research that could help make self-driving cars safer and more human-like. |
Keywords
* Artificial intelligence * Reinforcement learning
