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.

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