Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
This paper introduces STRELGen, a framework that combines a diffusion model with Spatio-Temporal Logic (STREL) specifications. By enabling differentiable monitoring of satisfaction levels, it performs gradient-based optimization to efficiently generate safety-critical driving scenarios that are both plausible and within the learned data distribution.
[2605.19038] Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
[Submitted on 18 May 2026]
Title:Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
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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.
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2605.19038 [cs.RO]
(or arXiv:2605.19038v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.19038
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Francesco Giacomarra [view email] [v1] Mon, 18 May 2026 19:00:14 UTC (4,948 KB)
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