arXiv:2501.17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect. In multi-agent simulation, the primary challenges include behavioral multimodality and closed-loop distributional shifts. In this study, we formulate a unified mixture model (UniMM) framework for generating multimodal agent behaviors, which can cover the mainstream methods including regression-based mixture models and discrete NTP models. Furthermore, we introduce a closed-loop sample generation app
The increasing complexity of autonomous systems, particularly in driving, necessitates more sophisticated and realistic simulation environments for accurate assessment and development.
Improved multi-agent simulation frameworks are critical infrastructure for the safe and efficient development of autonomous AI systems, accelerating their deployment and reliability.
This unified mixture model (UniMM) offers a more robust and adaptable approach to generating multimodal and closed-loop behaviors in multi-agent simulations, potentially standardizing how such systems are tested.
- · Autonomous vehicle developers
- · AI research institutions
- · Simulation software providers
- · Robotics companies
- · Companies relying on less sophisticated simulation methods
- · Human testers in certain scenarios
More accurate and faster development cycles for autonomous driving systems.
Reduced testing costs and improved safety validation for AI-powered agents in complex environments.
Accelerated adoption of AI agents in real-world scenarios due to enhanced pre-deployment reliability and predictive capabilities.
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Read at arXiv cs.AI