SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction

• URL: http://arxiv.org/abs/2405.03809v1
• Date: Mon, 6 May 2024 19:47:23 GMT
• Title: SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction
• Authors: Zixu Wang, Zhigang Sun, Juergen Luettin, Lavdim Halilaj,
• Abstract summary: SocialFormer is an agent interaction-aware trajectory prediction method. We present a temporal encoder based on gated recurrent units (GRU) to model the temporal social behavior of agent movements. We evaluate SocialFormer for the trajectory prediction task on the popular nuScenes benchmark and achieve state-of-the-art performance.
• Score: 3.733790302392792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving. However, most existing methods overlook complex interactions between traffic participants that often govern their future trajectories. In this paper, we propose SocialFormer, an agent interaction-aware trajectory prediction method that leverages the semantic relationship between the target vehicle and surrounding vehicles by making use of the road topology. We also introduce an edge-enhanced heterogeneous graph transformer (EHGT) as the aggregator in a graph neural network (GNN) to encode the semantic and spatial agent interaction information. Additionally, we introduce a temporal encoder based on gated recurrent units (GRU) to model the temporal social behavior of agent movements. Finally, we present an information fusion framework that integrates agent encoding, lane encoding, and agent interaction encoding for a holistic representation of the traffic scene. We evaluate SocialFormer for the trajectory prediction task on the popular nuScenes benchmark and achieve state-of-the-art performance.

Related papers

• Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems [2.498836880652668]
  Anticipating the behavior of an agent in a given situation is required to adequately react to it in time. Deep learning-based models has become the dominant approach to motion prediction recently. For longer prediction horizons, the deviation of the predicted trajectory from the ground truth is lower compared to a spatially and socially agnostic model.
  arXiv  Detail & Related papers  (2024-06-04T20:36:16Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding [121.08841110022607]
  Existing agent-centric methods have demonstrated outstanding performance on public benchmarks. We introduce the K-nearest neighbor attention with relative pose encoding (KNARPE), a novel attention mechanism allowing the pairwise-relative representation to be used by Transformers. By sharing contexts among agents and reusing the unchanged contexts, our approach is as efficient as scene-centric methods, while performing on par with state-of-the-art agent-centric methods.
  arXiv  Detail & Related papers  (2023-10-19T17:59:01Z)
• Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction [53.473846742702854]
  We propose a recurrent, attention-based approach for motion forecasting. Decoder Fusion RNN (DF-RNN) is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.
  arXiv  Detail & Related papers  (2021-08-12T15:53:37Z)
• Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes [2.7955111755177695]
  We present a Spatial-Channel Transformer Network for trajectory prediction with attention functions. A channel-wise module is inserted to measure the social interaction between agents. We find that the network achieves promising results on real-world trajectory prediction datasets on the traffic scenes.
  arXiv  Detail & Related papers  (2021-01-27T15:03:42Z)
• Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph [4.574413934477815]
  We propose to use a hybrid graph whose nodes represent both the traffic actors as well as the static and dynamic traffic elements present in the scene. The different modes of temporal interaction (e.g., stopping and going) among actors and traffic elements are explicitly modeled by graph edges. We show that our proposed model, TrafficGraphNet, achieves state-of-the-art trajectory prediction accuracy while maintaining a high level of interpretability.
  arXiv  Detail & Related papers  (2020-09-27T18:20:03Z)
• End-to-end Contextual Perception and Prediction with Interaction Transformer [79.14001602890417]
  We tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer architecture. Our model can be trained end-to-end, and runs in real-time.
  arXiv  Detail & Related papers  (2020-08-13T14:30:12Z)
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
  In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
  arXiv  Detail & Related papers  (2020-07-23T14:31:25Z)
• Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism [57.68557165836806]
  We propose a model to predict the trajectories of target agents around an autonomous vehicle. We encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease.
  arXiv  Detail & Related papers  (2020-07-06T03:48:08Z)
• 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.