Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Saeed Rahmani, Shiva Rasouli, Daphne Cornelisse, Eugene Vinitsky, Bart van Arem, Simeon C. Calvert
Why It Matters
What makes this one worth your time
This paper is significant because it addresses the critical need for accurate simulation of mixed traffic environments, which is essential for the safe deployment and testing of autonomous vehicles.
A survey of AI methods for mixed autonomy traffic simulation with a new taxonomy.
Summary
This paper provides a comprehensive survey of AI methods for simulating mixed automated and human traffic, addressing a gap in existing literature by offering a unified taxonomy and bridging the fields of traffic engineering and computer science.
Key contributions
- Introduces a unified taxonomy for AI methods in mixed autonomy traffic simulation.
Notable insights
- The integration of AI into traffic simulation can significantly enhance the realism and accuracy of modeling complex driving behaviors.
Possible limitations
- The survey may not cover the very latest AI advancements due to the rapid pace of research in this field.
Abstract
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. 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. We introduce a taxonomy that organizes methods into three families: agent-level behavior models, environment-level simulation methods, and cognitive and physics-informed methods. The survey analyzes how existing simulation platforms fall short of the needs of mixed autonomy research and outlines directions to narrow this gap. It also provides a chronological overview of AI methods and reviews evaluation protocols and metrics, simulation tools, and datasets. By covering both traffic engineering and computer science perspectives, we aim to bridge the gap between these two communities.