Neural Interaction Energy for Multi-Agent Trajectory Prediction

• URL: http://arxiv.org/abs/2404.16579v1
• Date: Thu, 25 Apr 2024 12:47:47 GMT
• Title: Neural Interaction Energy for Multi-Agent Trajectory Prediction
• Authors: Kaixin Shen, Ruijie Quan, Linchao Zhu, Jun Xiao, Yi Yang,
• Abstract summary: We introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE) MATE assesses the interactive motion of agents by employing neural interaction energy. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint.
• Score: 55.098754835213995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maintaining temporal stability is crucial in multi-agent trajectory prediction. Insufficient regularization to uphold this stability often results in fluctuations in kinematic states, leading to inconsistent predictions and the amplification of errors. In this study, we introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE). This framework assesses the interactive motion of agents by employing neural interaction energy, which captures the dynamics of interactions and illustrates their influence on the future trajectories of agents. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint. These constraints work together to ensure temporal stability at both the system and agent levels, effectively mitigating prediction fluctuations inherent in multi-agent systems. Comparative evaluations against previous methods on four diverse datasets highlight the superior prediction accuracy and generalization capabilities of our model.

Related papers

• MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty [7.402769693163035]
  This paper introduces a Multi-modal Diffusion model for Motion Prediction (MDMP) It integrates skeletal data and textual descriptions of actions to generate refined long-term motion predictions with quantifiable uncertainty. Our model consistently outperforms existing generative techniques in accurately predicting long-term motions.
  arXiv  Detail & Related papers  (2024-10-04T18:49:00Z)
• Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network [1.5888246742280365]
  Trajectory prediction is crucial for autonomous driving as it aims to forecast future movements of traffic participants. Traditional methods usually perform holistic inference on trajectories of agents, neglecting the differences in difficulty among agents. This paper proposes a novel DifficultyGuided Feature Enhancement (DGFNet), which leverages the prediction difficulty differences among agents.
  arXiv  Detail & Related papers  (2024-07-26T07:04:30Z)
• Disentangled Neural Relational Inference for Interpretable Motion Prediction [38.40799770648501]
  We develop a variational auto-encoder framework that integrates graph-based representations and timesequence models. Our model infers dynamic interaction graphs augmented with interpretable edge features that characterize the interactions. We validate our approach through extensive experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-01-07T22:49:24Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
  The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans. We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
  arXiv  Detail & Related papers  (2022-11-16T20:00:38Z)
• Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction [32.970169015894705]
  We formulate an attention model for multi-agent interactions based on a total variation temporal smoothness prior. We show its advantages in terms of prediction accuracy by comparing it with the state-of-the-art approaches on both synthetic and naturalistic driving data.
  arXiv  Detail & Related papers  (2022-03-08T21:54:28Z)
• Dyadic Human Motion Prediction [119.3376964777803]
  We introduce a motion prediction framework that explicitly reasons about the interactions of two observed subjects. Specifically, we achieve this by introducing a pairwise attention mechanism that models the mutual dependencies in the motion history of the two subjects. This allows us to preserve the long-term motion dynamics in a more realistic way and more robustly predict unusual and fast-paced movements.
  arXiv  Detail & Related papers  (2021-12-01T10:30:40Z)
• RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting [34.54878390622877]
  We propose a generic motion forecasting framework with dynamic key information selection and ranking based on a hybrid attention mechanism. The framework is instantiated to handle multi-agent trajectory prediction and human motion forecasting tasks. We validate the framework on both synthetic simulations and motion forecasting benchmarks in different domains.
  arXiv  Detail & Related papers  (2021-08-03T06:30:30Z)
• Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction [97.40338982628094]
  We propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN) which predicts trajectories of heterogeneous agents in multiply categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously. A hierarchical graph attention module is proposed to obtain category-tocategory interaction and agent-to-agent interaction.
  arXiv  Detail & Related papers  (2021-07-31T13:36:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.