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ScenaGen: A CP Model for Grounding Qualitative Driving Scenarios

Authors Nassim Belmecheri , Arnaud Gotlieb , Nadjib Lazaar , Helge Spieker

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Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Artificial intelligence
Keywords
  • Constraint Programming
  • Scenario Grounding
  • Qualitative Reasoning
  • Autonomous Driving
  • Application

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Abstract

Validating Automated Driving Systems (ADS) requires generating various kinematically executable traffic scenarios. The grounding of qualitative descriptions into concrete trajectories is a combinatorial task poorly addressed by learning-based methods. We propose ScenaGen, a CP model operating on qualitative explainable graphs (QXGs) to encode spatio-temporal relations between traffic entities. Formulated over integer position variables, ScenaGen enforces qualitative spatial constraints, distance thresholds, and inter-frame kinematic consistency. A single QXG acts as a formal template for systematically enumerating distinct, quantitatively varied concrete scenarios. Evaluation of synthetic and real-world benchmarks demonstrates that ScenaGen provides a robust and efficient alternative for scenario instantiation, outperforming standard search baselines in both scalability and solution diversity.

Cite As Get BibTex

Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, and Helge Spieker. ScenaGen: A CP Model for Grounding Qualitative Driving Scenarios. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 4:1-4:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.4

Author Details

Nassim Belmecheri
  • Simula Research Laboratory, Oslo, Norway
Arnaud Gotlieb
  • Simula Research Laboratory, Oslo, Norway
Nadjib Lazaar
  • LISN, Université Paris-Saclay, Paris, France
Helge Spieker
  • Simula Research Laboratory, Oslo, Norway

Funding

This work is funded by the European Commission through the AI4CCAM project (Trustworthy AI for Connected, Cooperative Automated Mobility; grant agreement No 101076911) and also in part by the DeMik project through the UPS Excellences Springboard program (grant SPATTR-2025-12).

Supplementary Materials

References

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