基于信号时序逻辑的多样化可控扩散策略

Diverse Controllable Diffusion Policy with Signal Temporal Logic

March 4, 2025

作者: Yue Meng, Chuchu fan

cs.AI

摘要

生成逼真的仿真对于自动驾驶和人机交互等自主系统应用至关重要。然而，当前的驾驶模拟器在生成可控、多样且符合规则的交通参与者行为方面仍面临挑战：基于规则的方法无法产生多样化的行为且需要精细调参，而基于学习的方法虽能从数据中模仿策略，却未明确设计为遵循规则。此外，现实世界的数据集本质上是“单一结果”的，这使得学习方法难以生成多样化的行为。本文中，我们利用信号时序逻辑（STL）和扩散模型来学习可控、多样且规则感知的策略。我们首先在真实数据上校准STL，然后通过轨迹优化生成多样化的合成数据，最后在增强的数据集上学习修正后的扩散策略。我们在NuScenes数据集上进行了测试，与其他基线方法相比，我们的方法能够生成最多样化且符合规则的轨迹，运行时间仅为次优方法的1/17。在闭环测试中，我们的方法达到了最高的多样性、规则满足率以及最低的碰撞率。我们的方法能够根据不同的STL参数在测试中生成具有不同特征的轨迹。在人机相遇场景的案例研究中，我们的方法能够生成多样化且接近理想轨迹的结果。标注工具、增强数据集及代码可在https://github.com/mengyuest/pSTL-diffusion-policy获取。

English

Generating realistic simulations is critical for autonomous system applications such as self-driving and human-robot interactions. However, driving simulators nowadays still have difficulty in generating controllable, diverse, and rule-compliant behaviors for road participants: Rule-based models cannot produce diverse behaviors and require careful tuning, whereas learning-based methods imitate the policy from data but are not designed to follow the rules explicitly. Besides, the real-world datasets are by nature "single-outcome", making the learning method hard to generate diverse behaviors. In this paper, we leverage Signal Temporal Logic (STL) and Diffusion Models to learn controllable, diverse, and rule-aware policy. We first calibrate the STL on the real-world data, then generate diverse synthetic data using trajectory optimization, and finally learn the rectified diffusion policy on the augmented dataset. We test on the NuScenes dataset and our approach can achieve the most diverse rule-compliant trajectories compared to other baselines, with a runtime 1/17X to the second-best approach. In the closed-loop testing, our approach reaches the highest diversity, rule satisfaction rate, and the least collision rate. Our method can generate varied characteristics conditional on different STL parameters in testing. A case study on human-robot encounter scenarios shows our approach can generate diverse and closed-to-oracle trajectories. The annotation tool, augmented dataset, and code are available at https://github.com/mengyuest/pSTL-diffusion-policy.