标题： 基于引导潜扩散模型的安全关键交通仿真 显示英文标题

标题： Safety-Critical Traffic Simulation with Guided Latent Diffusion Model

摘要： 安全关键交通仿真在评估自动驾驶系统在罕见和具有挑战性的情景下扮演着至关重要的角色。然而，现有方法由于对物理合理性考虑不足，往往生成不现实的场景，并且存在生成效率低的问题。 为了解决这些限制，我们提出了一种指导潜扩散模型（LDM），能够生成物理上真实的对抗性安全关键交通场景。 具体来说，我们的模型采用基于图的变分自编码器（VAE）来学习一个紧凑的潜在空间，该空间捕捉复杂的多智能体交互，同时提高计算效率。 在这个潜在空间内，扩散模型执行去噪过程以生成逼真的轨迹。 为了实现可控的对抗性场景生成，我们引入了新的引导目标，推动扩散过程产生对抗性和行为上逼真的驾驶行为。 此外，我们开发了一个基于物理可行性检查的样本选择模块，进一步增强生成场景的物理合理性。 在nuScenes数据集上的广泛实验表明，与现有基线相比，我们的方法实现了优越的对抗效果和生成效率，同时保持了高度的真实性。 我们的工作提供了一个有效的工具来进行现实的安全关键场景仿真，为更稳健地评估自动驾驶系统铺平了道路。 显示英文摘要

摘要： Safety-critical traffic simulation plays a crucial role in evaluating autonomous driving systems under rare and challenging scenarios. However, existing approaches often generate unrealistic scenarios due to insufficient consideration of physical plausibility and suffer from low generation efficiency. To address these limitations, we propose a guided latent diffusion model (LDM) capable of generating physically realistic and adversarial safety-critical traffic scenarios. Specifically, our model employs a graph-based variational autoencoder (VAE) to learn a compact latent space that captures complex multi-agent interactions while improving computational efficiency. Within this latent space, the diffusion model performs the denoising process to produce realistic trajectories. To enable controllable and adversarial scenario generation, we introduce novel guidance objectives that drive the diffusion process toward producing adversarial and behaviorally realistic driving behaviors. Furthermore, we develop a sample selection module based on physical feasibility checks to further enhance the physical plausibility of the generated scenarios. Extensive experiments on the nuScenes dataset demonstrate that our method achieves superior adversarial effectiveness and generation efficiency compared to existing baselines while maintaining a high level of realism. Our work provides an effective tool for realistic safety-critical scenario simulation, paving the way for more robust evaluation of autonomous driving systems.