Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction

📄 arXiv: 2506.05810v1 📥 PDF

作者: Yesheng Zhang, Wenjian Sun, Yuheng Chen, Qingwei Liu, Qi Lin, Rui Zhang, Xu Zhao

分类: cs.AI, cs.RO

发布日期: 2025-06-06

备注: 10 pages

💡 一句话要点

提出轨迹熵以解决多智能体游戏状态稳定性问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 轨迹熵 多智能体系统 层级k博弈 自动驾驶 游戏状态建模 不确定性量化 信号处理

📋 核心要点

  1. 现有层级k博弈框架忽视了智能体间驾驶复杂性差异和状态动态变化，导致计算冗余和错误。
  2. 提出轨迹熵度量，通过提取多模态轨迹预测结果中的不确定性信号，量化智能体的游戏状态。
  3. 在Waymo和nuPlan数据集上进行评估，预测精度提高19.89%，规划精度提高16.48%，显示出优越性能。

📝 摘要（中文）

复杂的智能体交互为真实场景下的自动驾驶带来了显著挑战。近期，一种将智能体交互建模为层级k博弈框架的方法逐渐受到关注，但该框架忽视了智能体之间的驾驶复杂性差异及状态动态变化，导致冗余和易错的计算。为此，本文提出了一种名为轨迹熵的度量，揭示智能体在层级k博弈框架中的游戏状态。轨迹熵通过提取多模态轨迹预测结果中的不确定性统计信号，并利用信噪比量化智能体的游戏状态。基于轨迹熵，我们通过简单的门控机制优化了现有的层级k博弈框架，显著提高了整体准确性并降低了计算成本。实验结果表明，该方法在Waymo和nuPlan数据集上实现了最先进的性能，预测精度提高了19.89%，规划精度提高了16.48%。

🔬 方法详解

问题定义：本文旨在解决现有层级k博弈框架中对智能体间复杂交互的建模不足，特别是忽视了智能体驾驶复杂性和状态变化的问题，导致计算效率低下和准确性不足。

核心思路：通过引入轨迹熵这一新度量，识别智能体策略的不确定性与驾驶复杂性之间的内在关系，从而更准确地反映游戏状态。

技术框架：整体方法包括轨迹熵的计算、信号提取和门控机制的应用。首先，从多模态轨迹预测结果中提取统计信号，然后利用信噪比量化游戏状态，最后通过门控机制优化博弈框架。

关键创新：轨迹熵作为新度量，能够有效揭示智能体的游戏状态，显著改善了层级k博弈框架的性能，区别于传统方法的统一处理方式。

关键设计：在实现中，采用了特定的损失函数来优化轨迹熵的计算，并设计了适应性门控机制以提高模型的灵活性和准确性。具体参数设置和网络结构细节在实验部分进行了详细描述。

📊 实验亮点

实验结果显示，基于轨迹熵的方法在Waymo和nuPlan数据集上实现了最先进的性能，预测精度提高了19.89%，规划精度提高了16.48%。与基线方法相比，显著提升了整体准确性，同时降低了计算成本。

🎯 应用场景

该研究在自动驾驶、智能交通系统等领域具有广泛的应用潜力。通过更准确地建模智能体间的交互，能够提升自动驾驶系统的决策能力和安全性，推动智能交通技术的发展。未来，轨迹熵的概念也可能扩展到其他多智能体系统的研究中。

📄 摘要（原文）

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.