ChatGPT, developed by OpenAI, is one of the milestone large language models (LLMs) with 6 billion parameters. ChatGPT has demonstrated the impressive language understanding capability of LLM, particularly in generating conversational response. As LLMs start to gain more attention in various research or engineering domains, it is time to envision how LLM may revolutionize the way we approach intelligent transportation systems. This paper explores the future applications of LLM in addressing key transportation problems. By leveraging LLM with cross-modal encoder, an intelligent system can also process traffic data from different modalities and execute transportation operations through an LLM. We present and validate these potential transportation applications equipped by LLM. To further demonstrate this potential, we also provide a concrete smartphone-based crash report auto-generation and analysis framework as a use case. Despite the potential benefits, challenges related to data privacy, data quality, and model bias must be considered. Overall, the use of LLM in intelligent transport systems holds promise for more efficient, intelligent, and sustainable transportation systems that further improve daily life around the world.
翻译:ChatGPT 是由 OpenAI 开发的拥有 60 亿参数的里程碑式大型语言模型之一,展示了大型语言模型在生成对话响应方面出色的语言理解能力。随着大型语言模型在各种研究或工程领域越来越受关注,现在是时候设想大型语言模型如何在智能交通系统中革命性地改变我们的方法。本文探讨了大型语言模型在解决关键交通问题方面的未来应用。通过利用具有跨模态编码器的大型语言模型,智能系统还可以处理来自不同模态的交通数据,并通过大型语言模型执行交通操作。我们展示并验证了大型语言模型的这些潜在交通应用。为了进一步证明这一潜力,我们还提供了一个基于智能手机的事故报告自动生成和分析框架作为应用案例。尽管有潜在的好处,但必须考虑与数据隐私、数据质量和模型偏差相关的挑战。总体而言,大型语言模型在智能交通系统中的使用有望实现更加高效、智能和可持续的交通系统,进一步改善全球日常生活。