Cloud-Enhanced Generative Pipelines for Rapid Simulation of Driving Edge Cases

Abstract

Cloud-Enhanced Generative Pipelines for Rapid Simulation of Driving Edge Cases addresses a critical challenge in autonomous vehicle (AV) development: the scarcity of realistic, diverse, and complex driving edge‑case scenarios. Traditional data collection methods—real-world driving or manually created simulations—are costly, time-consuming, or limited in variety. We propose a cloud-enabled generative pipeline that leverages scalable cloud infrastructure, generative adversarial networks (GANs), and procedural scenario composition to rapidly synthesize high-fidelity driving edge‑case scenarios. This pipeline orchestrates three main modules: (1) a scenario generator that combines semantic and procedural elements (e.g., weather, lighting, pedestrian behavior, unusual road events); (2) a validation module that uses physics-based and safety-rule checks; and (3) cloud-based rendering and simulation execution that produces metrics-rich outputs. Our system is capable of synthesizing hundreds of edge cases per hour with parameter control and repeatability. We demonstrate the pipeline using a cloud cluster to simulate rare but critical conditions—such as sudden pedestrian dart, near-collision at obscured intersections under heavy rain, and abrupt obstacle appearance in low visibility. Results show that the generated scenarios exhibit high realism (as judged by domain experts) and can meaningfully stress test perception, planning, and control modules of AV software. We report significant reductions (80%+) in scenario generation time and cost compared to manual or physically recorded methods. The proposed pipeline paves the way for scalable, customizable, and reproducible validation environments, accelerating AV safety validation and regulatory compliance. While our initial focus is on perception edge cases, the approach generalizes to multi-agent behaviors and closed-loop testing. Future extensions include tighter human-in-the-loop adjustments, adaptive generative tuning based on failure feedback, and integration with real-time digital twins.