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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2312.08248 |
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| _version_ | 1866909377305247744 |
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| author | Yan, Huan Li, Yong |
| author_facet | Yan, Huan Li, Yong |
| contents | Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems (ITS), such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in ITS tailored specifically for road transportation. First, we introduce the principles of different generative AI techniques. Then, we classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to intelligent transportation systems, and discuss future research directions based on different application scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08248 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective Yan, Huan Li, Yong Artificial Intelligence Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems (ITS), such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in ITS tailored specifically for road transportation. First, we introduce the principles of different generative AI techniques. Then, we classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to intelligent transportation systems, and discuss future research directions based on different application scenarios. |
| title | A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2312.08248 |