Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning

• URL: http://arxiv.org/abs/2409.11676v1
• Date: Wed, 18 Sep 2024 03:30:38 GMT
• Title: Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
• Authors: Keshu Wu, Yang Zhou, Haotian Shi, Dominique Lord, Bin Ran, Xinyue Ye,
• Abstract summary: Real-world driving environments are characterized by dynamic and diverse interactions among vehicles. This research introduces an integrated framework for autonomous vehicles (AVs) motion prediction.
• Score: 13.294396870431399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents considerable challenges in accurately predicting the motion states of vehicles and handling the uncertainty inherent in the predictions. Addressing these challenges requires comprehensive modeling and reasoning to capture the implicit relations among vehicles and the corresponding diverse behaviors. This research introduces an integrated framework for autonomous vehicles (AVs) motion prediction to address these complexities, utilizing a novel Relational Hypergraph Interaction-informed Neural mOtion generator (RHINO). RHINO leverages hypergraph-based relational reasoning by integrating a multi-scale hypergraph neural network to model group-wise interactions among multiple vehicles and their multi-modal driving behaviors, thereby enhancing motion prediction accuracy and reliability. Experimental validation using real-world datasets demonstrates the superior performance of this framework in improving predictive accuracy and fostering socially aware automated driving in dynamic traffic scenarios.

Related papers

• Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction [0.6202955567445396]
  We present a novel trajectory prediction model for autonomous driving. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. The proposed model showcases strong potential for application in real-world autonomous driving systems.
  arXiv  Detail & Related papers  (2024-11-25T15:03:44Z)
• FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction [9.2729178775419]
  This study introduces a scaled noise conditional diffusion model for car-following trajectory prediction. It integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving the accuracy and plausibility of predicted trajectories. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.
  arXiv  Detail & Related papers  (2024-11-23T23:13:45Z)
• Neural Interaction Energy for Multi-Agent Trajectory Prediction [55.098754835213995]
  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.
  arXiv  Detail & Related papers  (2024-04-25T12:47:47Z)
• 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)
• 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)
• Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks [17.989423104706397]
  This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction framework. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. We employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings.
  arXiv  Detail & Related papers  (2023-09-05T06:28:13Z)
• MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying [110.83590008788745]
  Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. In this paper, we propose Motion TRansformer (MTR) frameworks to address these challenges. The initial MTR framework utilizes a transformer encoder-decoder structure with learnable intention queries. We introduce an advanced MTR++ framework, extending the capability of MTR to simultaneously predict multimodal motion for multiple agents.
  arXiv  Detail & Related papers  (2023-06-30T16:23:04Z)
• Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference [68.8204255655161]
  We propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories. We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories.
  arXiv  Detail & Related papers  (2021-04-19T09:41:31Z)
• 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)
• 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)
• 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.