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arXiv 2510.08046cs.AI
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LinguaSim：基于大语言模型的自然语言指令交互式多车辆测试场景生成

LinguaSim: Interactive Multi-Vehicle Testing Scenario Generation via Natural Language Instruction Based on Large Language Models

National Key R&D Program of China（中华人民共和国国家重点研发计划）

Qingyuan Shi, Qingwen Meng, Hao Cheng, Qing Xu, Jianqiang Wang

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AI总结：

LinguaSim基于大语言模型将自然语言指令转化为交互式三维驾驶场景，通过反馈校准提升生成精度，显著降低碰撞率并增强安全测试效果。

摘要：

自动驾驶车辆测试和训练场景的生成已引起广泛关注。虽然大语言模型（LLMs）已催生出新的场景生成方法，但现有方法难以在指令遵循准确性与真实驾驶环境的逼真度之间取得平衡。为降低场景描述复杂度，这些方法常通过将场景限制为二维或开环模拟（其中背景车辆遵循预定义的、非交互的行为）来牺牲逼真度。我们提出LinguaSim，一个基于LLM的框架，可将自然语言转换为逼真的、交互式的三维场景，确保动态车辆交互以及输入描述与生成场景之间的忠实对齐。反馈校准模块进一步细化生成精度，提高对用户意图的保真度。通过弥合自然语言与闭环交互模拟之间的差距，LinguaSim利用场景描述和引导车辆的自动驾驶模型来约束对抗性车辆行为。该框架有助于创建高保真场景，从而增强安全测试和训练。实验表明，LinguaSim能生成与不同自然语言描述对齐的不同关键度场景（危险描述下ACT为0.072秒，安全描述下为3.532秒；舒适度分别为0.654和0.764），且其细化模块有效降低LinguaSim初始输出中的过度攻击性，将碰撞率从46.9%降至6.3%，以更好地匹配用户意图。

英文摘要：

The generation of testing and training scenarios for autonomous vehicles has drawn significant attention. While Large Language Models (LLMs) have enabled new scenario generation methods, current methods struggle to balance command adherence accuracy with the realism of real-world driving environments. To reduce scenario description complexity, these methods often compromise realism by limiting scenarios to 2D, or open-loop simulations where background vehicles follow predefined, non-interactive behaviors. We propose LinguaSim, an LLM-based framework that converts natural language into realistic, interactive 3D scenarios, ensuring both dynamic vehicle interactions and faithful alignment between the input descriptions and the generated scenarios. A feedback calibration module further refines the generation precision, improving fidelity to user intent. By bridging the gap between natural language and closed-loop, interactive simulations, LinguaSim constrains adversarial vehicle behaviors using both the scenario description and the autonomous driving model guiding them. This framework facilitates the creation of high-fidelity scenarios that enhance safety testing and training. Experiments show LinguaSim can generate scenarios with varying criticality aligned with different natural language descriptions (ACT: 0.072 s for dangerous vs. 3.532 s for safe descriptions; comfortability: 0.654 vs. 0.764), and its refinement module effectively reduces excessive aggressiveness in LinguaSim's initial outputs, lowering the crash rate from 46.9% to 6.3% to better match user intentions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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