Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics

TL;DR

This paper presents a novel state-space and control inference framework for multi-agent trajectory prediction, integrating graph neural networks and a new control modeling technique to improve accuracy and scalability.

Contribution

It introduces the Mixed Mamba control model and combines it with graph neural networks within a state-space framework, advancing multi-agent trajectory forecasting.

Findings

Outperforms existing benchmarks in accuracy

Effectively captures multi-agent interactions

Demonstrates scalability across scenarios

Abstract

In the field of autonomous systems, accurately predicting the trajectories of nearby vehicles and pedestrians is crucial for ensuring both safety and operational efficiency. This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling, which endows agents with models that have tangible physical implications. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. This technique utilizes a newly introduced model, termed "Mixed Mamba," to derive initial control states, thereby improving the predictive accuracy of these variables. Moverover, the proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions. This combination provides a robust and scalable framework for forecasting multi-agent trajectories across a range of scenarios. Comprehensive evaluations demonstrate that this model outperforms several established benchmarks across various metrics and datasets, highlighting its significant potential to advance trajectory forecasting in autonomous systems.