Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic

• URL: http://arxiv.org/abs/2605.19038v1
• Date: Mon, 18 May 2026 19:00:14 GMT
• Title: Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
• Abstract summary: We introduce STRELGen, a framework for the targeted generation of safety-critical driving scenarios.<n>Monitoring satisfaction levels of these specifications is differentiable, enabling gradient-based search.<n> STRELGen thus provides a flexible, interpretable, and powerful tool for stress-testing autonomous driving systems.
• Score: 3.071933369858584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of autonomous driving (AD) technologies has outpaced the development of robust safety evaluation methods. Conventional testing relies on exposing AD systems to vast numbers of real-world traffic scenes -- a brute-force approach that is prohibitively expensive and statistically ineffective at capturing the rare, safety-critical edge cases essential for validating real-world robustness. To address this fundamental limitation, we introduce STRELGen, a scalable framework for the targeted generation of safety-critical driving scenarios. STRELGen synergistically combines a multi-agent trajectory-generation diffusion model (DM) with Spatio-Temporal Logic (STREL) specifications that encode complex safety and realism properties through a highly interpretable formalism. Crucially, monitoring satisfaction levels of these specifications is differentiable, enabling gradient-based search. At inference time, we optimize directly over the DM latent space to maximize STREL formula satisfaction. The result is efficient generation of highly plausible yet safety-critical multi-agent scenarios that lie within the learned data distribution. STRELGen thus provides a flexible, interpretable, and powerful tool for stress-testing autonomous driving systems, moving beyond the limitations of brute-force data collection.

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