GraphSCENE: On-demand critical scenario generation for autonomous vehicles in simulation

Panagiotaki, Efimia; Pramatarov, Georgi; Kunze, Lars; De Martini, Daniele

Abstract

Testing and validating Autonomous Vehicle (AV) performance in safety-critical and diverse scenarios is crucial before real-world deployment. However, manually creating such scenarios in simulation remains a significant and time-consuming challenge. This work introduces a novel method that generates dynamic temporal scene graphs corresponding to diverse traffic scenarios, on-demand, tailored to user-defined preferences, such as AV actions, sets of dynamic agents, and criticality levels. A temporal Graph Neural Network (GNN) model learns to predict relationships between ego-vehicle, agents, and static structures, guided by real-world spatiotem-poral interaction patterns and constrained by an ontology that restricts predictions to semantically valid links. Our model consistently outperforms the baselines in accurately generating links corresponding to the requested scenarios. We render the predicted scenarios in simulation to further demonstrate their effectiveness as testing environments for AV agents.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
• Start Date: Oct 19, 2025
• End Date: Oct 25, 2025
• Acceptance Date: Jun 16, 2025
• Deposit Date: Aug 22, 2025
• Peer Reviewed: Peer Reviewed
• Keywords: Scenarios generation; graph generation; dynamic temporal scene graphs; heterogeneous graph learning
• Public URL: https://uwe-repository.worktribe.com/output/14832711
• Other Repo URL: https://ora.ox.ac.uk/objects/uuid:d44b5997-6809-4237-8d05-dc78e0238588