V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction

• URL: http://arxiv.org/abs/2412.01812v1
• Date: Mon, 02 Dec 2024 18:55:34 GMT
• Title: V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
• Authors: Zewei Zhou, Hao Xiang, Zhaoliang Zheng, Seth Z. Zhao, Mingyue Lei, Yun Zhang, Tianhui Cai, Xinyi Liu, Johnson Liu, Maheswari Bajji, Jacob Pham, Xin Xia, Zhiyu Huang, Bolei Zhou, Jiaqi Ma,
• Abstract summary: Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems.<n>Prior work primarily focuses on single-frame cooperative perception.<n>In this paper, we focus on temporal perception and prediction tasks in V2X scenarios and one-step and multi-temporal communication strategies.
• Score: 43.506717060709136
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems. Prior work primarily focuses on single-frame cooperative perception, which fuses agents' information across different spatial locations but ignores temporal cues and temporal tasks (e.g., temporal perception and prediction). In this paper, we focus on temporal perception and prediction tasks in V2X scenarios and design one-step and multi-step communication strategies (when to transmit) as well as examine their integration with three fusion strategies - early, late, and intermediate (what to transmit), providing comprehensive benchmarks with various fusion models (how to fuse). Furthermore, we propose V2XPnP, a novel intermediate fusion framework within one-step communication for end-to-end perception and prediction. Our framework employs a unified Transformer-based architecture to effectively model complex spatiotemporal relationships across temporal per-frame, spatial per-agent, and high-definition map. Moreover, we introduce the V2XPnP Sequential Dataset that supports all V2X cooperation modes and addresses the limitations of existing real-world datasets, which are restricted to single-frame or single-mode cooperation. Extensive experiments demonstrate our framework outperforms state-of-the-art methods in both perception and prediction tasks.

Related papers

• V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Reality [13.68645389910716]
  We introduce V2X-ReaLO, an open online cooperative perception framework deployed on real vehicles and smart infrastructure. We present an open benchmark dataset designed to assess the performance of online cooperative perception systems.
  arXiv  Detail & Related papers  (2025-03-13T04:31:20Z)
• Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving [16.479343520119073]
  Co-MTP is a general cooperative trajectory prediction framework with multi-temporal fusion for autonomous driving. In the future domain, V2X can provide the prediction results of surrounding objects. We evaluate the Co-MTP framework on the real-world dataset V2X-Seq.
  arXiv  Detail & Related papers  (2025-02-23T14:38:13Z)
• DeepInteraction++: Multi-Modality Interaction for Autonomous Driving [80.8837864849534]
  We introduce a novel modality interaction strategy that allows individual per-modality representations to be learned and maintained throughout. DeepInteraction++ is a multi-modal interaction framework characterized by a multi-modal representational interaction encoder and a multi-modal predictive interaction decoder. Experiments demonstrate the superior performance of the proposed framework on both 3D object detection and end-to-end autonomous driving tasks.
  arXiv  Detail & Related papers  (2024-08-09T14:04:21Z)
• Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving [4.628774934971078]
  Current research on trajectory prediction primarily relies on data collected by onboard sensors of an ego vehicle. We introduce V2INet, a novel trajectory prediction framework designed to model multi-view data by extending existing single-view models. Our results demonstrate superior performance in terms of Final Displacement Error (FDE) and Miss Rate (MR) using a single GPU.
  arXiv  Detail & Related papers  (2024-08-01T08:32:03Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection [9.967263440745432]
  Occlusion is a major challenge for LiDAR-based object detection methods. State-of-the-art V2X methods resolve the performance-bandwidth tradeoff using a mid-collaboration approach. We devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior methods.
  arXiv  Detail & Related papers  (2023-07-04T03:49:42Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)
• Full-Duplex Strategy for Video Object Segmentation [141.43983376262815]
  Full- Strategy Network (FSNet) is a novel framework for video object segmentation (VOS) Our FSNet performs the crossmodal feature-passing (i.e., transmission and receiving) simultaneously before fusion decoding stage. We show that our FSNet outperforms other state-of-the-arts for both the VOS and video salient object detection tasks.
  arXiv  Detail & Related papers  (2021-08-06T14:50:50Z)
• 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.