ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction

TL;DR

ViTE introduces a flexible framework combining virtual nodes and an expert router to adaptively model pedestrian interactions, achieving state-of-the-art trajectory prediction without deep GNN stacks.

Contribution

The paper presents ViTE, a novel approach that adaptively models both explicit and implicit interactions using virtual nodes and a Mixture-of-Experts router, avoiding deep GNNs.

Findings

Achieves state-of-the-art results on ETH/UCY, NBA, and SDD benchmarks.

Effectively models high-order interactions without deep GNN stacks.

Demonstrates practical efficiency and scalability.

Abstract

Pedestrian trajectory prediction is critical for ensuring safety in autonomous driving, surveillance systems, and urban planning applications. While early approaches primarily focus on one-hop pairwise relationships, recent studies attempt to capture high-order interactions by stacking multiple Graph Neural Network (GNN) layers. However, these approaches face a fundamental trade-off: insufficient layers may lead to under-reaching problems that limit the model's receptive field, while excessive depth can result in prohibitive computational costs. We argue that an effective model should be capable of adaptively modeling both explicit one-hop interactions and implicit high-order dependencies, rather than relying solely on architectural depth. To this end, we propose ViTE (Virtual graph Trajectory Expert router), a novel framework for pedestrian trajectory prediction. ViTE consists of two key modules: a Virtual Graph that introduces dynamic virtual nodes to model long-range and high-order interactions without deep GNN stacks, and an Expert Router that adaptively selects interaction experts based on social context using a Mixture-of-Experts design. This combination enables flexible and scalable reasoning across varying interaction patterns. Experiments on three benchmarks (ETH/UCY, NBA, and SDD) demonstrate that our method consistently achieves state-of-the-art performance, validating both its effectiveness and practical efficiency.