Spectral Temporal Graph Neural Network for Trajectory Prediction

• URL: http://arxiv.org/abs/2106.02930v1
• Date: Sat, 5 Jun 2021 16:51:54 GMT
• Title: Spectral Temporal Graph Neural Network for Trajectory Prediction
• Authors: Defu Cao and Jiachen Li and Hengbo Ma and Masayoshi Tomizuka
• Abstract summary: An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobile robots. Previous works focused on utilizing the spatial and temporal information in time domain while not sufficiently taking advantage of the cues in frequency domain. We propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain.
• Score: 24.212525264346887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobile robots. This task is challenging since the behavior of an autonomous agent is not only affected by its own intention, but also by the static environment and surrounding dynamically interacting agents. Previous works focused on utilizing the spatial and temporal information in time domain while not sufficiently taking advantage of the cues in frequency domain. To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain. SpecTGNN operates on both an agent graph with dynamic state information and an environment graph with the features extracted from context images in two streams. The model integrates graph Fourier transform, spectral graph convolution and temporal gated convolution to encode history information and forecast future trajectories. Moreover, we incorporate a multi-head spatio-temporal attention mechanism to mitigate the effect of error propagation in a long time horizon. We demonstrate the performance of SpecTGNN on two public trajectory prediction benchmark datasets, which achieves state-of-the-art performance in terms of prediction accuracy.

Related papers

• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting [33.538633286142264]
  Time-Shift of traffic patterns and noise induced by random factors hinder data-driven spatial dependence modeling. We propose a novel dynamic frequency domain graph convolution network (DFDGCN) to capture spatial dependencies. Our model is effective and outperforms the baselines in experiments on four real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T08:20:09Z)
• Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction [16.530361912832763]
  We propose a temporal graph neural point process framework, named STNPP, for traffic congestion event prediction. Our method achieves superior performance in comparison to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-11-15T01:22:47Z)
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective [48.00240550685946]
  Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. We propose a novel Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. Our experiments on seven datasets have demonstrated superior performance with higher efficiency and fewer parameters compared with state-of-the-
  arXiv  Detail & Related papers  (2023-11-10T17:13:26Z)
• Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation [0.6650227510403052]
  Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADG-N) is proposed to improve traffic flow prediction in highway transportation. A dedicated gated kernel emphasizing highly relative nodes is introduced on complete graphs to reduce overfitting for graph convolution operations.
  arXiv  Detail & Related papers  (2023-09-13T13:57:21Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting [4.1700160312787125]
  We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
  arXiv  Detail & Related papers  (2022-06-07T09:08:35Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

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.