Summary of Lc-llm: Explainable Lane-change Intention and Trajectory Predictions with Large Language Models, by Mingxing Peng et al.
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
by Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu, Kehua Chen
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 A novel approach to lane change prediction in autonomous vehicles is proposed, leveraging Large Language Models (LLMs) and Chain-of-Thought (CoT) reasoning to improve long-term accuracy and interpretability. The Lane Change Long-Short Term Memory (LC-LLM) model reformulates the lane change prediction task as a language modeling problem, processing driving scenario information as natural language prompts for LLMs. This approach enables fine-tuning of LLMs for lane change prediction and provides CoT reasoning and explanations for predictions. Experimental results on the highD dataset demonstrate superior performance and interpretability of LC-LLM in lane change prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous vehicles need to predict where other cars will go. Right now, computers aren’t very good at this. They can get better by using special language models that are great at understanding human language. This paper shows how to use these models to predict where other cars will change lanes and why they will make those decisions. It’s like a game of “I Spy” for cars! The computer looks at all the things happening on the road, like what’s going on with the traffic lights and the weather, and uses that information to figure out what the other cars might do. This helps autonomous vehicles make better decisions and avoid accidents. |
Keywords
» Artificial intelligence » Fine tuning
