Diffusion-Based Urban Scene Generation and Risk Forecasting for Closed-Loop Autonomous Driving Simulation
Keywords:
diffusion models; autonomous driving; urban scene generation; risk forecasting; closed-loop simulation; socio-technical infrastructure; system architecture; uncertainty quantification; safety validation; policy governanceAbstract
The deployment of autonomous driving systems at scale demands simulation environments that can faithfully reproduce the complexity, variability, and risk characteristics of real urban traffic. Closed-loop simulation, in which the ego vehicle interacts dynamically with a reactive environment, is essential for validating decision-making policies before real-world deployment. This paper presents a comprehensive framework for diffusion-based urban scene generation coupled with risk forecasting within closed-loop autonomous driving simulation. Unlike static scenario databases or rule-based traffic simulators, the proposed approach leverages denoising diffusion probabilistic models to generate high-fidelity, controllable urban scenes that include road layouts, dynamic agents, and environmental conditions. A parallel risk forecasting module integrates uncertainty quantification, causal reasoning, and predictive hazard assessment to anticipate collision events and traffic violations. We examine the system architecture from a socio-technical perspective, addressing structural trade-offs between generative fidelity and computational tractability, the governance of simulation data pipelines, and the policy implications of using synthetic scenes for safety validation. The framework is situated within broader discussions on infrastructure scalability, robustness to distributional shift, fairness across demographic and geographic contexts, and the regulatory challenges of certifying autonomous systems through simulation. By synthesizing advances in generative modeling, probabilistic forecasting, and closed-loop evaluation, this work provides a blueprint for next-generation simulation platforms that are both technically rigorous and socially responsible.
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