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

2026-04-14

AI Summary

Existing traffic simulation tools struggle to accurately capture the complex interactions between human-driven and autonomous vehicles in mixed traffic environments and lack a systematic synthesis of relevant AI methodologies. This work proposes the first unified taxonomy that encompasses three categories of AI approaches: agent-level behavioral modeling, environment-level simulation, and integration of cognitive and physical information, thereby bridging the research gap between transportation engineering and computer science. By consolidating mainstream simulation platforms, datasets, and evaluation metrics, the study systematically analyzes the limitations of current tools, clarifies the evolutionary trajectory of AI methods in this domain, and puts forward a standardized evaluation protocol along with promising future research directions to advance high-fidelity mixed traffic simulation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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.

Problem

• mixed autonomy traffic
• simulation
• AI methods
• driving behavior modeling
• taxonomy

Innovation

• mixed autonomy traffic
• AI-driven simulation
• behavior modeling
• taxonomy of AI methods
• human-AV interaction