可调交互强度的环形交叉口场景生成方法
Scenario Generation in Roundabouts with Adjustable Interaction Intensity
- 用自编码器分离路径与时间特征，通过WGAN生成场景
- 通过缩放控制码λ调节交互强度，提升安全余量
- 适合智能驾驶系统安全测试与交互行为分析
环形交叉口因频繁的汇入与让行交互，是智能驾驶功能开发与测试中的安全关键场景。然而，从自然数据中提取足够近临界状态的场景效率低下。现有生成方法对交互强度和临界性的控制能力有限，难以支持系统性安全测试与深入分析。本文提出一种具备连续可调交互强度的交互感知环形交叉口场景生成器。首先利用预训练自编码器将几何路径与时间进度解耦并映射为隐空间编码；随后基于Wasserstein生成对抗网络（WGAN）进行条件隐空间生成。让行行为以紧凑的让行编码在进入前段进行可控的时间干预，交互强度通过缩放因子$λ$调节。结果表明，该方法在时序-隐空间保真度及交互响应合理性上优于基线模型。在临界性校准下，增大$λ$可扩展安全余量，实现可扩展、可控的安全测试机制。
原文摘要 · Abstract (English)
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.
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