用三阶段框架生成可控且含危险因素的交通场景
ML-SceGen: A Multi-level Scenario Generation Framework
- 分三阶段：语义转功能场景、逻辑推理生成交通流、大模型调参提升风险等级
- 在无管控交叉口生成包含危险因素的完整交通场景，覆盖复杂交互
- 适合自动驾驶测试与安全评估，尤其关注场景可控性与真实感
当前科研中，大语言模型用于场景生成多集中于全面或高危场景，缺乏可控性。本文提出一个三阶段框架，使用户能重新掌控生成过程，并在非受控交叉口生成包含危险因素的完整交通场景。第一阶段，通过大模型代理将预期场景描述的关键要素转化为功能场景；第二阶段，利用答案集规划（ASP）求解器Clingo生成交叉口内完整的逻辑交通流；第三阶段，借助大模型调整相关参数，提升具体场景的临界风险等级。该框架兼顾生成质量与用户控制力，适用于自动驾驶系统在复杂、危险情境下的验证。
原文摘要 · Abstract (English)
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.
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