Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multi-Agent Trajectory Prediction

TL;DR

This paper introduces a novel approach for multi-agent trajectory prediction that models heterogeneous interactions with reduced error accumulation, utilizing dynamic interaction graphs, a heterogeneous attention mechanism, and strategies like graph entropy and mixup training.

Contribution

It proposes a new heterogeneous interaction modeling framework with reduced accumulated error, addressing complex interactions in diverse multi-agent systems.

Findings

Outperforms existing methods on real-world datasets

Effectively reduces spatial and temporal error sources

Demonstrates superior trajectory prediction accuracy

Abstract

Dynamical complex systems composed of interactive heterogeneous agents are prevalent in the world, including urban traffic systems and social networks. Modeling the interactions among agents is the key to understanding and predicting the dynamics of the complex system, e.g., predicting the trajectories of traffic participants in the city. Compared with interaction modeling in homogeneous systems such as pedestrians in a crowded scene, heterogeneous interaction modeling is less explored. Worse still, the error accumulation problem becomes more severe since the interactions are more complex. To tackle the two problems, this paper proposes heterogeneous interaction modeling with reduced accumulated error for multi-agent trajectory prediction. Based on the historical trajectories, our method infers the dynamic interaction graphs among agents, featured by directed interacting relations and interacting effects. A heterogeneous attention mechanism is defined on the interaction graphs for aggregating the influence from heterogeneous neighbors to the target agent. To alleviate the error accumulation problem, this paper analyzes the error sources from the spatial and temporal perspectives, and proposes to introduce the graph entropy and the mixup training strategy for reducing the two types of errors respectively. Our method is examined on three real-world datasets containing heterogeneous agents, and the experimental results validate the superiority of our method.