Roundabouts, characterized by frequent merging and yielding interactions, remain a safety-critical corner case for the development and testing of intelligent driving functions. However, extracting sufficient near-critical scenarios from naturalistic data is inefficient. Most existing scenario generation methods provide limited controllability over interaction intensity and criticality, making systematic safety testing and detailed analysis difficult. This paper presents an interaction-aware roundabout scenario generator with continuously adjustable interaction intensity. Geometric routes and temporal progress profiles are first decoupled and mapped to latent codes using pretrained autoencoders. Conditional latent generation is then performed with Wasserstein Generative Adversarial Networks (WGAN) to generate scenarios. Yielding is modeled as a controllable timing intervention via a compact yield code during the approach-to-entry segment, where interaction intensity is modulated by scaling the code with a factor $λ$. Results demonstrate enhanced timing-latent fidelity and plausible interaction responses compared to a baseline model. Under criticality-calibrated scaling, increasing $λ$ expands the safety margin, providing a scalable and controlled testing mechanism.

翻译：环岛因其频繁的并流与让行交互行为，仍是智能驾驶功能开发与测试中的关键安全边界场景。然而，从自然驾驶数据中提取足够多的近临界场景效率较低。现有大多数场景生成方法对交互强度与临界性的可控性有限，导致系统性安全测试与详细分析困难。本文提出一种交互感知的环岛场景生成器，支持交互强度的连续可调。首先，利用预训练自编码器将几何路径与时间进程解耦并映射至隐空间编码；随后，采用Wasserstein生成对抗网络（WGAN）进行条件隐编码生成。通过紧凑的让行编码，将让行行为建模为进入路段段可控时序干预，并采用缩放因子$\lambda$对编码进行调制以改变交互强度。与基线模型相比，所提方法在时序-隐空间保真度与合理交互响应方面表现更优。在临界性校准缩放下，增大$\lambda$可扩展安全边界，从而提供可扩展且可控的测试机制。