Summary of Llm-assist: Enhancing Closed-loop Planning with Language-based Reasoning, by S P Sharan et al.
LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning
by S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this research paper, a team of experts explores the possibility of using Large Language Models (LLMs) like GPT4 and Llama2 to develop robust planning algorithms for autonomous vehicles. The current planners have limitations, with learning-based ones suffering from overfitting and poor performance in complex scenarios, while rule-based ones generalize well but struggle with nuanced driving situations. To overcome these challenges, the authors propose a hybrid planner that combines conventional rule-based planning with LLM-based reasoning to produce well-reasoned plans for self-driving vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using super smart computers to help self-driving cars make good decisions. Right now, the planners used in self-driving cars aren’t very good at handling tricky situations. Some are really good at learning from experience, but get stuck when things get complicated. Others are great at following rules, but don’t know how to handle complex maneuvers. The scientists in this study wanted to see if they could use special computers called Large Language Models (LLMs) to help make better decisions for self-driving cars. |
Keywords
» Artificial intelligence » Overfitting
