Summary of Testing Large Language Models on Driving Theory Knowledge and Skills For Connected Autonomous Vehicles, by Zuoyin Tang et al.
Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles
by Zuoyin Tang, Jianhua He, Dashuai Pei, Kezhong Liu, Tao Gao
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Networking and Internet Architecture (cs.NI); Robotics (cs.RO)
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 supporting autonomous driving systems is explored in this paper, which applies remote or edge large language models (LLMs) to handle long-tail corner cases. The primary challenge lies in assessing LLMs’ understanding of driving theory and skills, ensuring they are qualified for safety-critical tasks. To evaluate the performance of various proprietary and open-source LLM models, including OpenAI GPT, Baidu Ernie, Ali QWen, Tsinghua MiniCPM-2B, and MiniCPM-Llama3-V2.5, driving theory tests were designed and run with over 500 multiple-choice questions. The experiments measured model accuracy, cost, and processing latency. Results show that while GPT-4 passes the test with improved domain knowledge, Ernie has an accuracy of 85%, and other LLM models fail to meet the threshold. For image-based questions, GPT4-o achieves 96% accuracy, and MiniCPM-Llama3-V2.5 achieves 76%. The study highlights the potential trade-offs between model performance and cost, informing decisions on using existing LLMs for CAV applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous vehicles (AVs) face a big challenge: handling unusual corner cases. Researchers are exploring how large language models (LLMs) can help with this problem. But there’s a catch – these models require a lot of computing power and may not be very good at understanding driving theory. In this paper, scientists try to find out which LLMs are best for helping AVs make decisions. They designed special tests to see how well different LLMs understand driving rules and skills. The results show that some LLMs do better than others, but it’s not just about how well they perform – it’s also important to consider the cost of using each model. |
Keywords
» Artificial intelligence » Gpt
