LD-Scene: LLM誘導拡散モデルによる敵対的安全臨界運転シナリオの制御可能な生成
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
大規模言語モデルと潜在拡散モデルを組み合わせ、自然言語の指示から敵対的な安全臨界運転シナリオを制御可能に生成するフレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Mingxing Peng, Yuting Xie, Xusen Guo, Ruoyu Yao, Hai Yang, Jun Ma
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are rare and difficult to collect from real-world driving data, posing significant challenges to effectively assessing the performance of autonomous vehicles. Typical existing methods often suffer from limited controllability and lack user-friendliness, as extensive expert knowledge is essentially required. To address these challenges, we propose LD-Scene, a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) for user-controllable adversarial scenario generation through natural language. Our approach comprises an LDM that captures realistic driving trajectory distributions and an LLM-based guidance module that translates user queries into adversarial loss functions, facilitating the generation of scenarios aligned with user queries. The guidance module integrates an LLM-based Chain-of-Thought (CoT) code generator and an LLM-based code debugger, enhancing the controllability and robustness in generating guidance functions. Extensive experiments conducted on the nuScenes dataset demonstrate that LD-Scene achieves state-of-the-art performance in generating realistic, diverse, and effective adversarial scenarios. Furthermore, our framework provides fine-grained control over adversarial behaviors, thereby facilitating more effective testing tailored to specific driving scenarios.
