Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic

TL;DR

STRELGen is a scalable framework that combines diffusion models and spatio-temporal logic to generate safety-critical driving scenarios for autonomous vehicle testing.

Contribution

It introduces a novel method integrating multi-agent diffusion models with formal logic specifications for targeted scenario generation.

Findings

Enables efficient generation of safety-critical scenarios.

Uses differentiable satisfaction monitoring for optimization.

Produces plausible, safety-critical multi-agent traffic scenarios.

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.