Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic

arXiv cs.AI / 4/15/2026

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Key Points

The paper argues that while autonomous vehicle (AV) deployment makes testing and validation more critical, existing traffic simulation tools often emphasize graphics and use overly simple rule-based behavior models that cannot capture real driving complexity.
It presents a comprehensive survey focused specifically on AI methods for simulating mixed automated and human traffic, noting that prior surveys typically either reviewed simulation tools without detailing underlying AI methods or addressed decision-making in an ego-centric way.
The authors introduce a unified taxonomy of AI approaches, organizing methods into three families: agent-level behavior models, environment-level simulation methods, and cognitive/physics-informed methods.
The survey evaluates how current platforms fall short for mixed-autonomy research, reviews evaluation protocols, metrics, simulation tools, and datasets, and includes a chronological overview of relevant AI techniques.
It aims to bridge traffic engineering and computer science perspectives to help guide future directions for more accurate and interoperable mixed-traffic simulation.

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.