Summary of Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models, by Yizhou Huang et al.
Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models
by Yizhou Huang, Yihua Cheng, Kezhi Wang
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this field can describe driving scenes and behaviors with a level of accuracy similar to human perception, particularly in visual tasks. The paper proposes a driving behavior narration and reasoning framework that applies LLMs to edge devices. The framework consists of multiple roadside units, with LLMs deployed on each unit. These roadside units collect road data and communicate via 5G NSR/NR networks. Experiments show that LLMs deployed on edge devices can achieve satisfactory response speeds. Additionally, the paper proposes a prompt strategy to enhance the narration and reasoning performance of the system. This strategy integrates multi-modal information, including environmental, agent, and motion data. The approach is demonstrated on the OpenDV-Youtube dataset, showing significant improvements in performance across both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving technology is getting smarter with deep learning architectures that can reason like humans. Large language models are great at describing what’s happening on the road, but they need to be fast and local to work well in autonomous vehicles. The paper proposes a new system that puts these language models on edge devices near the road. This way, the system can process data quickly and send responses back without delay. To make it even better, the authors suggest a special strategy for getting the most out of this system by combining different types of data like what’s happening on the road, where the vehicles are, and how they’re moving. The results show that this approach works really well and can improve performance significantly. |
Keywords
» Artificial intelligence » Deep learning » Multi modal » Prompt
