Peng, Mingxing (The Hong Kong University of Science and Technology (Guangzhou)), Yao, Ruoyu (The Hong Kong University of Science and Technology (Guangzhou)), GUO, Xusen (The Hong Kong University of Science and Technology (Guangzhou)), Xie, Yuting (Sun Yat-sen University), CHEN, Xianda (HKUST(GZ)), Ma, Jun (The Hong Kong University of Science and Technology (Guangzhou))

Safety-Critical Traffic Simulation with Guided Latent Diffusion Model

Scheduled for presentation during the Invited Session "S19a-Artificial Transportation Systems and Simulation" (TH-LM-T19), Thursday, November 20, 2025, 11:30−11:50, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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. 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.