Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Summary: arXiv:2604.12857v1 Announce Type: new
Abstract
Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However, existing simulation tools mainly focus on graphical realism and rely on simple rule-based models and therefore fail to accurately represent the complexity of driving behaviors and interactions.
Artificial intelligence (AI) has shown strong potential to address these limitations; however, despite the rapid progress across AI methodologies, a comprehensive survey of their application to mixed autonomy traffic simulation remains lacking. Existing surveys either focus on simulation tools without examining the AI methods behind them, or cover ego-centric decision-making without addressing the broader challenge of modeling surrounding traffic. Moreover, they do not offer a unified taxonomy of AI methods covering individual behavior modeling to full scene simulation.
Objectives of the Survey
To address these gaps, this survey provides a structured review and synthesis of AI methods for modeling AV and human driving behavior in mixed autonomy traffic simulation. The following objectives are outlined:
- Introduce a taxonomy that organizes methods into three families:
- Agent-level behavior models
- Environment-level simulation methods
- Cognitive and physics-informed methods
- Analyze how existing simulation platforms fall short of the needs of mixed autonomy research.
- Outline directions to narrow the gap between current methods and practical applications.
- Provide a chronological overview of AI methods relevant to traffic simulation.
- Review evaluation protocols, metrics, simulation tools, and datasets.
Bridging Disciplines
This survey aims to bridge the gap between traffic engineering and computer science perspectives. By combining insights from these two communities, it seeks to enhance the understanding and effectiveness of simulation tools in a mixed traffic environment.
Conclusion
The integration of AI in traffic simulation is crucial for advancing the safe deployment of autonomous vehicles. By providing a comprehensive overview of current methodologies, this survey not only highlights the existing challenges but also opens up new avenues for research and development. As the field continues to evolve, the insights gained from this study could play a pivotal role in shaping the future of mixed automated and human traffic systems.