[2605.06966] Traffic Scenario Orchestration from Language via Constraint Satisfaction
[Submitted on 7 May 2026]
Title:Traffic Scenario Orchestration from Language via Constraint Satisfaction
View a PDF of the paper titled Traffic Scenario Orchestration from Language via Constraint Satisfaction, by Frieda Rong and 3 other authors
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Abstract:Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios are often out-of-distribution and have precise requirements on interactions with the AV policy under test. Manually programming scenarios allows for precise controllability but is difficult to scale. On the other hand, statistical models can leverage compute and data, but struggle with precise controllability when out-of-distribution. We cast scenario orchestration as a constraint-solving problem and present a language-in, simulation-out scenario orchestrator for closed-loop testing AVs. Our approach leverages foundation model reasoning to translate general, natural language descriptions into a set of constraints as a scenario representation. This then allows us to leverage off the shelf solvers to solve for actor behaviors which meet precise testing intentions in closed-loop. Under a benchmark of carefully crafted and diverse scenario descriptions, our approach greatly outperforms our baselines in orchestration success rate. We further show that our closed-loop approach is especially important for scenarios which require ego-reactive specifications.
Comments: 19 pages, 10 figures; full version of paper accepted for poster presentation at ICRA 2026
Subjects:
Robotics (cs.RO); Software Engineering (cs.SE)
Cite as: arXiv:2605.06966 [cs.RO]
(or arXiv:2605.06966v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.06966
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Frieda Rong [view email] [v1] Thu, 7 May 2026 21:34:27 UTC (3,186 KB)
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