Summary of Hybrid Llm-ddqn Based Joint Optimization Of V2i Communication and Autonomous Driving, by Zijiang Yan et al.
Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving
by Zijiang Yan, Hao Zhou, Hina Tabassum, Xue Liu
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); 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 The paper explores applying large language models (LLMs) to vehicular networks, aiming to optimize vehicle-to-infrastructure (V2I) communications and autonomous driving (AD) policies. LLMs are used for AD decision-making to maximize traffic flow and avoid collisions, while a double deep Q-learning algorithm (DDQN) is used for V2I optimization. The paper employs the Euclidean distance to identify previously explored AD experiences, allowing LLMs to learn from past decisions. The iterative optimization approach reveals the potential of using LLMs for network optimization and management. The proposed hybrid LLM-DDQN approach outperforms conventional DDQN, showing faster convergence and higher average rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computers called large language models (LLMs) to help make decisions about autonomous driving and how cars communicate with roads. This can make traffic flow better and prevent accidents. The computers also learn from their past experiences so they get better over time. The researchers combined these LLMs with another computer program called DDQN to optimize how cars communicate. They tested this combination and found it worked better than just using the other program alone. |
Keywords
» Artificial intelligence » Euclidean distance » Optimization
