Future-Aware Interaction Network For Motion Forecasting

• URL: http://arxiv.org/abs/2503.06565v1
• Date: Sun, 09 Mar 2025 11:38:34 GMT
• Title: Future-Aware Interaction Network For Motion Forecasting
• Authors: Shijie Li, Xun Xu, Si Yong Yeo, Xulei Yang,
• Abstract summary: We propose an interaction-based method, named Future-Aware Interaction Network, that introduces potential future trajectories into scene encoding.<n>To adapt Mamba for spatial interaction modeling, we propose an adaptive reordering strategy that transforms unordered data into a structured sequence.<n>Mamba is employed to refine generated future trajectories temporally, ensuring more consistent predictions.
• Score: 10.211526610529374
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Motion forecasting is a crucial component of autonomous driving systems, enabling the generation of accurate and smooth future trajectories to ensure safe navigation to the destination. In previous methods, potential future trajectories are often absent in the scene encoding stage, which may lead to suboptimal outcomes. Additionally, prior approaches typically employ transformer architectures for spatiotemporal modeling of trajectories and map information, which suffer from the quadratic scaling complexity of the transformer architecture. In this work, we propose an interaction-based method, named Future-Aware Interaction Network, that introduces potential future trajectories into scene encoding for a comprehensive traffic representation. Furthermore, a State Space Model (SSM), specifically Mamba, is introduced for both spatial and temporal modeling. To adapt Mamba for spatial interaction modeling, we propose an adaptive reordering strategy that transforms unordered data into a structured sequence. Additionally, Mamba is employed to refine generated future trajectories temporally, ensuring more consistent predictions. These enhancements not only improve model efficiency but also enhance the accuracy and diversity of predictions. We conduct comprehensive experiments on the widely used Argoverse 1 and Argoverse 2 datasets, demonstrating that the proposed method achieves superior performance compared to previous approaches in a more efficient way. The code will be released according to the acceptance.

Related papers

• Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM [16.532357621144342]
  This paper introduces Trajectory Mamba, a novel efficient trajectory prediction framework based on the selective state-space model (SSM) To address the potential reduction in prediction accuracy resulting from modifications to the attention mechanism, we propose a joint polyline encoding strategy. Our model achieves state-of-the-art results in terms of inference speed and parameter efficiency on both the Argoverse 1 and Argoverse 2 datasets.
  arXiv  Detail & Related papers  (2025-03-13T21:31:12Z)
• AdaOcc: Adaptive Forward View Transformation and Flow Modeling for 3D Occupancy and Flow Prediction [56.72301849123049]
  We present our solution for the Vision-Centric 3D Occupancy and Flow Prediction track in the nuScenes Open-Occ dataset challenge at CVPR 2024. Our innovative approach involves a dual-stage framework that enhances 3D occupancy and flow predictions by incorporating adaptive forward view transformation and flow modeling. Our method combines regression with classification to address scale variations in different scenes, and leverages predicted flow to warp current voxel features to future frames, guided by future frame ground truth.
  arXiv  Detail & Related papers  (2024-07-01T16:32:15Z)
• OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment [0.0]
  We introduce an end-to-end neural network methodology designed to predict the future behaviors of all dynamic objects in the environment. We propose a novel time-weighted motion flow loss, whose application has shown a substantial decrease in end-point error.
  arXiv  Detail & Related papers  (2024-04-02T19:37:58Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• Motion Transformer with Global Intention Localization and Local Movement Refinement [103.75625476231401]
  Motion TRansformer (MTR) models motion prediction as the joint optimization of global intention localization and local movement refinement. MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges.
  arXiv  Detail & Related papers  (2022-09-27T16:23:14Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction [12.84508682310717]
  We propose LatentFormer, a transformer-based model for predicting future vehicle trajectories. We evaluate the proposed method on the nuScenes benchmark dataset and show that our approach achieves state-of-the-art performance and improves upon trajectory metrics by up to 40%.
  arXiv  Detail & Related papers  (2022-03-03T17:44:58Z)
• 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)
• AMENet: Attentive Maps Encoder Network for Trajectory Prediction [35.22312783822563]
  Trajectory prediction is critical for applications of planning safe future movements. We propose an end-to-end generative model named Attentive Maps Network (AMENet) AMENet encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction.
  arXiv  Detail & Related papers  (2020-06-15T10:00:07Z)

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.