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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.10782 |
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| _version_ | 1866912193546551296 |
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| author | Xiao, Yicheng Sun, Yangyang Lin, Yicheng |
| author_facet | Xiao, Yicheng Sun, Yangyang Lin, Yicheng |
| contents | Current scientific research witnesses various attempts at applying Large Language Models for scenario generation but is inclined only to comprehensive or dangerous scenarios. In this paper, we seek to build a three-stage framework that not only lets users regain controllability over the generated scenarios but also generates comprehensive scenarios containing danger factors in uncontrolled intersection settings. In the first stage, LLM agents will contribute to translating the key components of the description of the expected scenarios into Functional Scenarios. For the second stage, we use Answer Set Programming (ASP) solver Clingo to help us generate comprehensive logical traffic within intersections. During the last stage, we use LLM to update relevant parameters to increase the critical level of the concrete scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10782 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ML-SceGen: A Multi-level Scenario Generation Framework Xiao, Yicheng Sun, Yangyang Lin, Yicheng Artificial Intelligence Current scientific research witnesses various attempts at applying Large Language Models for scenario generation but is inclined only to comprehensive or dangerous scenarios. In this paper, we seek to build a three-stage framework that not only lets users regain controllability over the generated scenarios but also generates comprehensive scenarios containing danger factors in uncontrolled intersection settings. In the first stage, LLM agents will contribute to translating the key components of the description of the expected scenarios into Functional Scenarios. For the second stage, we use Answer Set Programming (ASP) solver Clingo to help us generate comprehensive logical traffic within intersections. During the last stage, we use LLM to update relevant parameters to increase the critical level of the concrete scenario. |
| title | ML-SceGen: A Multi-level Scenario Generation Framework |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10782 |