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This paper introduces Video2Track, a novel framework that transforms real-world driving videos into dynamic, steerable adversarial closed-track testing scenarios for automated driving systems (ADS). By integrating a scenario semantic mapping module with a dynamic interactive testing module, the framework captures the complexity of real-world interactions and generates diverse multi-agent trajectories while controlling interaction intensity. Experimental results show that Video2Track can effectively reproduce realistic driving scenarios and create scenario variants with adjustable risk levels, enhancing the validation process for ADS.
Real-world driving scenarios can now be seamlessly translated into controllable closed-track tests, enabling more realistic validation of automated driving systems.
Closed-track testing plays a fundamental role in the verification and validation of automated driving systems (ADS), particularly for safety-critical scenarios, by enabling reproducible evaluation under controlled conditions. However, most existing approaches still rely on standardized protocols or predefined trajectories, leading to overly scripted interactions and limited ability to reproduce the natural complexity of public-road traffic. To address this limitation, we propose Video2Track, a framework that transfers real-world interactive driving scenarios from videos into steerable adversarial closed-track testing. The framework consists of two tightly coupled modules. The first is a scenario semantic mapping module, which extracts structured semantics from driving videos using a vision-language model and grounds them onto a closed-track topology library via retrieval-augmented generation, thereby identifying compatible map segments and interaction anchors. The second is a dynamic interactive testing module, which conditions on the grounded topology and anchors to generate diverse multi-agent trajectories through a conditional diffusion model, while regulating interaction intensity via a Stackelberg game with a parameterized adversarial objective. Closed-track experiments demonstrate that the proposed framework can faithfully reproduce representative real-world interaction scenarios and generate executable scenario variants with controllable risk levels and interaction styles, providing a scalable approach for realistic and steerable ADS validation.
