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

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

URL: http://arxiv.org/abs/2408.12609v1
Date: Thu, 8 Aug 2024 08:33:02 GMT
Title: Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics
Authors: Yu Zhang, Yongxiang Zou, Haoyu Zhang, Zeyu Liu, Houcheng Li, Long Cheng,
Abstract summary: This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. The proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions.
Score: 14.694200929205975
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.