Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception

• URL: http://arxiv.org/abs/2506.06474v1
• Date: Fri, 06 Jun 2025 18:58:04 GMT
• Title: Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
• Authors: Everett Richards, Bipul Thapa, Lena Mashayekhy,
• Abstract summary: This research highlights the potential of edge computing to enhance collaborative perception for latency-sensitive autonomous systems.<n>We introduce an innovative framework, Edge-Enabled Collaborative Object Detection (ECOD) for CAVs, that leverages edge computing and multi-CAV collaboration for real-time, multi-perspective object detection.<n>Our experimental results demonstrate the significant benefits of ECOD in terms of improved object classification accuracy, outperforming traditional single-perspective onboard approaches by up to 75%, while ensuring low-latency, edge-driven real-time processing.
• Score: 1.2289361708127877
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and reliable object detection is critical for ensuring the safety and efficiency of Connected Autonomous Vehicles (CAVs). Traditional on-board perception systems have limited accuracy due to occlusions and blind spots, while cloud-based solutions introduce significant latency, making them unsuitable for real-time processing demands required for autonomous driving in dynamic environments. To address these challenges, we introduce an innovative framework, Edge-Enabled Collaborative Object Detection (ECOD) for CAVs, that leverages edge computing and multi-CAV collaboration for real-time, multi-perspective object detection. Our ECOD framework integrates two key algorithms: Perceptive Aggregation and Collaborative Estimation (PACE) and Variable Object Tally and Evaluation (VOTE). PACE aggregates detection data from multiple CAVs on an edge server to enhance perception in scenarios where individual CAVs have limited visibility. VOTE utilizes a consensus-based voting mechanism to improve the accuracy of object classification by integrating data from multiple CAVs. Both algorithms are designed at the edge to operate in real-time, ensuring low-latency and reliable decision-making for CAVs. We develop a hardware-based controlled testbed consisting of camera-equipped robotic CAVs and an edge server to evaluate the efficacy of our framework. Our experimental results demonstrate the significant benefits of ECOD in terms of improved object classification accuracy, outperforming traditional single-perspective onboard approaches by up to 75%, while ensuring low-latency, edge-driven real-time processing. This research highlights the potential of edge computing to enhance collaborative perception for latency-sensitive autonomous systems.

Related papers

• Towards Edge-Based Idle State Detection in Construction Machinery Using Surveillance Cameras [0.0]
  Underused construction machinery leads to increased operational costs and project delays.<n>This paper presents the Edge-IMI framework for detecting idle construction machinery.<n>The proposed solution consists of three components: object detection, tracking, and idle state identification.
  arXiv  Detail & Related papers  (2025-06-01T08:43:33Z)
• Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception [24.300126250046894]
  Collaborative perception is envisioned to improve perceptual accuracy by using vehicle-to-everything (V2X) communication.<n>Due to limited communication resources, it is impractical for all units to transmit sensing data such as point clouds or high-definition video.<n>We propose a deep reinforcement learning-based V2X user scheduling algorithm for collaborative perception.
  arXiv  Detail & Related papers  (2025-02-12T04:45:00Z)
• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• Sparse Federated Training of Object Detection in the Internet of Vehicles [13.864554148921826]
  Object detection is one of the key technologies in the Internet of Vehicles (IoV) Current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server. We propose a federated learning-based framework, where well-trained local models are shared in the central server.
  arXiv  Detail & Related papers  (2023-09-07T08:58:41Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection [2.064612766965483]
  Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. We propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method.
  arXiv  Detail & Related papers  (2020-10-22T00:12:58Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)

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.