Safety-Critical Traffic Simulation with Guided Latent Diffusion Model

Safety-Critical Traffic Simulation with Guided Latent Diffusion Model 1 st Mingxing Peng The Hong Kong University of Science and T echnology (Guangzhou) Guangzhou, China mpeng060@connect.hkust-gz.edu.cn 2 nd Ruoyu Y ao The Hong Kong University of Science and T echnology (Guangzhou) Guangzhou, China ryao092@connect.hkust-gz.edu.cn 3 rd Xusen Guo The Hong Kong University of Science and T echnology (Guangzhou) Guangzhou, China xguo796@connect.hkust-gz.edu.cn 4 th Y uting Xie School of Computer Science and Engineering Sun Y at-sen University Guangzhou, China xieyt8@mail2.sysu.edu.cn 5 th Xianda Chen The Hong Kong University of Science and T echnology (Guangzhou) Guangzhou, China xchen595@connect.hkust-gz.edu.cn Abstract --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. T o 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 (V AE) 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. T o enable controllable and adversarial scenario generation, we introduce novel guidance objectives that drive the diffusion process toward producing adversarial and behaviorally realistic driving behaviors.