The field of autonomous traffic safety analysis is moving towards the development of more sophisticated digital twin frameworks, which enable high-fidelity simulations of complex traffic environments. These frameworks integrate various data sources, such as LiDAR, GPS, and sensor data, to generate realistic 3D road geometries and simulate real-world driving scenarios. The use of AI-enabled models, such as hypergraph-based AI and guided latent diffusion models, is also becoming increasingly popular for predicting probabilistic trajectories and generating physically realistic safety-critical traffic scenarios. Noteworthy papers in this area include: Virtual Roads, Smarter Safety, which presents a digital-twin platform for active safety analysis in mixed traffic environments. AI2-Active Safety, which introduces an AI-enabled interaction-aware active safety analysis framework that accounts for groupwise vehicle interactions. Safety-Critical Traffic Simulation with Guided Latent Diffusion Model, which proposes a guided latent diffusion model for generating physically realistic and adversarial safety-critical traffic scenarios.