Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction

2025-06-06

AI Summary

Existing level-k game-theoretic frameworks for multi-agent interaction modeling in autonomous driving overlook heterogeneity in driving complexity and the dynamic nature of game states, leading to computational redundancy and insufficient robustness. To address this, we propose a trajectory entropy metric that, for the first time, establishes an intrinsic link between strategic uncertainty and driving complexity. We further design a signal-to-noise ratio–based trajectory entropy indicator to enable adaptive game-level state perception, and introduce a lightweight gating mechanism to optimize computational efficiency. Evaluated on the Waymo Open Motion and nuPlan datasets, our approach achieves up to 19.89% improvement in trajectory prediction accuracy and up to 16.48% gains in both open-loop and closed-loop planning performance, while significantly reducing computational overhead.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Complex interactions among agents present a significant challenge for autonomous driving in real-world scenarios. Recently, a promising approach has emerged, which formulates the interactions of agents as a level-k game framework. It effectively decouples agent policies by hierarchical game levels. However, this framework ignores both the varying driving complexities among agents and the dynamic changes in agent states across game levels, instead treating them uniformly. Consequently, redundant and error-prone computations are introduced into this framework. To tackle the issue, this paper proposes a metric, termed as Trajectory Entropy, to reveal the game status of agents within the level-k game framework. The key insight stems from recognizing the inherit relationship between agent policy uncertainty and the associated driving complexity. Specifically, Trajectory Entropy extracts statistical signals representing uncertainty from the multimodality trajectory prediction results of agents in the game. Then, the signal-to-noise ratio of this signal is utilized to quantify the game status of agents. Based on the proposed Trajectory Entropy, we refine the current level-k game framework through a simple gating mechanism, significantly improving overall accuracy while reducing computational costs. Our method is evaluated on the Waymo and nuPlan datasets, in terms of trajectory prediction, open-loop and closed-loop planning tasks. The results demonstrate the state-of-the-art performance of our method, with precision improved by up to 19.89% for prediction and up to 16.48% for planning.

Problem

• Modeling game state stability in autonomous driving scenarios
• Quantifying agent policy uncertainty via trajectory entropy
• Improving level-k game framework accuracy and efficiency

Innovation

• Trajectory Entropy models agent game status
• Gating mechanism refines level-k framework
• Signal-to-noise ratio quantifies driving complexity