Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data

2026-01-29

AI Summary

This work addresses a critical limitation in existing autonomous driving algorithms: their reliance on microscopic driving data that lacks macroscopic traffic context, while macroscopic data alone cannot be readily linked to individual vehicle behaviors. To bridge this gap, the paper proposes the first unified framework that jointly models microscopic vehicle trajectories and macroscopic traffic statistics. By integrating partially observed microscopic state reconstruction with population-level statistical alignment, the approach leverages imitation learning and reinforcement learning to impose dual constraints during policy optimization—ensuring fidelity to both individual ground-truth trajectories and emergent macroscopic traffic patterns. The resulting driving policies generate behaviors consistent with real-world traffic flow when deployed, thereby enabling safe and scalable human–autonomy collaboration in complex traffic environments.

Technology Category

• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

A driving algorithm that aligns with good human driving practices, or at the very least collaborates effectively with human drivers, is crucial for developing safe and efficient autonomous vehicles. In practice, two main approaches are commonly adopted: (i) supervised or imitation learning, which requires comprehensive naturalistic driving data capturing all states that influence a vehicle's decisions and corresponding actions, and (ii) reinforcement learning (RL), where the simulated driving environment either matches or is intentionally more challenging than real-world conditions. Both methods depend on high-quality observations of real-world driving behavior, which are often difficult and costly to obtain. State-of-the-art sensors on individual vehicles can gather microscopic data, but they lack context about the surrounding conditions. Conversely, roadside sensors can capture traffic flow and other macroscopic characteristics, but they cannot associate this information with individual vehicles on a microscopic level. Motivated by this complementarity, we propose a framework that reconstructs unobserved microscopic states from macroscopic observations, using microscopic data to anchor observed vehicle behaviors, and learns a shared policy whose behavior is microscopically consistent with the partially observed trajectories and actions and macroscopically aligned with target traffic statistics when deployed population-wide. Such constrained and regularized policies promote realistic flow patterns and safe coordination with human drivers at scale.

Problem

• microscopic driving behavior
• macroscopic traffic statistics
• partial observation
• behavior reconstruction
• autonomous driving

Innovation

• micro-macro alignment
• driving behavior reconstruction
• partial observability
• traffic statistics
• shared driving policy