A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction

• URL: http://arxiv.org/abs/2606.14956v1
• Date: Fri, 12 Jun 2026 20:57:27 GMT
• Title: A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
• Abstract summary: Graph Neural Networks (GNNs) have become a promising approach for driving interactions among road agents.<n>GNNs for trajectory prediction remains non-standardized, with little guidance on which graph layers effectively capture spatial interactions and temporal dynamics.<n>This paper offers a detailed comparative study of 19 graph layer types to discover the most effective architectures for trajectory prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement. Graph Neural Networks (GNNs) have become a promising approach for modelling spatiotemporal interactions among road agents. However, designing GNN architectures for trajectory prediction remains non-standardized, with little guidance on which graph layers effectively capture spatial interactions and temporal dynamics. This paper offers a detailed comparative study of 19 graph layer types, focusing on their spatial and temporal processing capabilities to discover the most effective architectures for trajectory prediction. Within the explored hyperparameter setting, we highlight five standout layer combinations, with ARMA, Chebyshev, and topology-aware layers consistently performing better than others. Beyond performance metrics, our findings yield practical design principles: sum-based aggregation is more effective than mean-based methods, multi-head attention mechanisms enable richer interactions, and assigning different weights to different hop distances significantly improves prediction accuracy. These findings offer useful guidance for designing more interpretable and effective trajectory prediction models.

Related papers

• Learning Long-Horizon Predictions for Quadrotor Dynamics [48.08477275522024]
  We study the key design choices for efficiently learning long-horizon prediction dynamics for quadrotors. We show that sequential modeling techniques showcase their advantage in minimizing compounding errors compared to other types of solutions. We propose a novel decoupled dynamics learning approach, which further simplifies the learning process while also enhancing the approach modularity.
  arXiv  Detail & Related papers  (2024-07-17T19:06:47Z)
• Hallmarks of Optimization Trajectories in Neural Networks: Directional Exploration and Redundancy [75.15685966213832]
  We analyze the rich directional structure of optimization trajectories represented by their pointwise parameters. We show that training only scalar batchnorm parameters some while into training matches the performance of training the entire network.
  arXiv  Detail & Related papers  (2024-03-12T07:32:47Z)
• SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases [66.61789780666727]
  We show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. We also offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states. Our model yields a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-08-25T07:15:58Z)
• A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction [4.181632607997678]
  We propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction. In the DL stage, the traffic scene is divided into multiple intermediate-scale heterogenous graphs based on which Transformer-style GNNs are adopted to encode heterogenous interactions. In the RL stage, we divide the traffic scene into local sub-scenes utilizing the key future points predicted in the DL stage.
  arXiv  Detail & Related papers  (2023-03-22T02:47:42Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Adaptive Trajectory Prediction via Transferable GNN [74.09424229172781]
  We propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain invariant GNN is proposed to explore the structural motion knowledge where the domain specific knowledge is reduced. An attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representation for knowledge transfer.
  arXiv  Detail & Related papers  (2022-03-09T21:08:47Z)
• Trajectory Prediction with Graph-based Dual-scale Context Fusion [43.51107329748957]
  We present a graph-based trajectory prediction network named the Dual Scale Predictor. It encodes both the static and dynamical driving context in a hierarchical manner. Thanks to the proposed dual-scale context fusion network, our DSP is able to generate accurate and human-like multi-modal trajectories.
  arXiv  Detail & Related papers  (2021-11-02T13:42:16Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking [23.608125748229174]
  We propose a generic generative neural system for multi-agent trajectory prediction involving heterogeneous agents. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2021-02-18T02:25:35Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction [5.346782918364054]
  We propose a novel CNN-based spatial-temporal graph framework GraphCNT to support more efficient and accurate trajectory predictions. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-16T12:56:12Z)
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network [29.289670231364788]
  In this paper, we propose a generic generative neural system for multi-agent trajectory prediction. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2020-02-14T20:11:13Z)

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.