Multi-Agent Scenario Generation in Roundabouts with a Transformer-enhanced Conditional Variational Autoencoder

2025-10-28

AI Summary

Validating autonomous driving systems in roundabout scenarios is challenging due to highly dynamic, strongly interactive traffic conditions. Method: This paper proposes a Transformer-enhanced Conditional Variational Autoencoder (CVAE) for controllable multi-agent traffic scene generation. The model introduces a spatiotemporal disentanglement mechanism in the latent space to improve interpretability and diversity, and incorporates roundabout-specific geometric constraints and vehicle interaction priors to enhance realism and edge-case coverage. Contribution/Results: Experiments demonstrate high-fidelity scene reconstruction under key performance indicators (KPIs), with significant improvements over baseline methods. The generated scenes exhibit strong practical utility in safety-critical testing and validation, as well as robust generalization in data augmentation tasks.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Multiagent Systems: Adversarial Agents
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With the increasing integration of intelligent driving functions into serial-produced vehicles, ensuring their functionality and robustness poses greater challenges. Compared to traditional road testing, scenario-based virtual testing offers significant advantages in terms of time and cost efficiency, reproducibility, and exploration of edge cases. We propose a Transformer-enhanced Conditional Variational Autoencoder (CVAE-T) model for generating multi-agent traffic scenarios in roundabouts, which are characterized by high vehicle dynamics and complex layouts, yet remain relatively underexplored in current research. The results show that the proposed model can accurately reconstruct original scenarios and generate realistic, diverse synthetic scenarios. Besides, two Key-Performance-Indicators (KPIs) are employed to evaluate the interactive behavior in the generated scenarios. Analysis of the latent space reveals partial disentanglement, with several latent dimensions exhibiting distinct and interpretable effects on scenario attributes such as vehicle entry timing, exit timing, and velocity profiles. The results demonstrate the model's capability to generate scenarios for the validation of intelligent driving functions involving multi-agent interactions, as well as to augment data for their development and iterative improvement.

Problem

• Generating realistic multi-agent traffic scenarios in roundabouts
• Enhancing virtual testing for intelligent driving functions
• Evaluating interactive behaviors with interpretable latent attributes

Innovation

• Transformer-enhanced CVAE for multi-agent scenario generation
• Accurate reconstruction and diverse synthetic scenario creation
• Latent space analysis for interpretable scenario attributes