Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction

TL;DR

This paper introduces Trajectory Entropy, a metric that quantifies agent uncertainty in multimodal trajectory prediction, enhancing the level-k game framework for autonomous driving by improving accuracy and reducing computational costs.

Contribution

It proposes Trajectory Entropy to better model agent uncertainty and refines the level-k game framework with a gating mechanism, achieving state-of-the-art results.

Findings

Improved trajectory prediction accuracy by up to 19.89%.

Enhanced planning performance with up to 16.48% gains.

Reduced computational costs in the game framework.

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.