Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation

• URL: http://arxiv.org/abs/2302.03128v1
• Date: Mon, 6 Feb 2023 21:30:08 GMT
• Title: Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation
• Authors: Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
• Abstract summary: We formulate a universal CP system into an optimization problem and a mobile-edge-cloud framework called Cooperverse. A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints. Experiments have been conducted based on a high-fidelity CP platform and the results show that the Cooperverse framework is effective for dynamic node engagement.
• Score: 15.195933965761645
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cooperative perception (CP) is attracting increasing attention and is regarded as the core foundation to support cooperative driving automation, a potential key solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. However, current research on CP is still at the beginning stages where a systematic problem formulation of CP is still missing, acting as the essential guideline of the system design of a CP system under real-world situations. In this paper, we formulate a universal CP system into an optimization problem and a mobile-edge-cloud framework called Cooperverse. This system addresses CP in a mixed connectivity and automation environment. A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance. Experiments have been conducted based on a high-fidelity CP platform, and the results show that the Cooperverse framework is effective for dynamic node engagement and the proposed DFS methodology can improve system CP performance by 14.5% and the RPF method can reduce the communication cost for mobile nodes by 90% with only 1.7% drop for average precision.

Related papers

• DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model [44.23276350463173]
  Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. We propose DiffCP, a novel CP paradigm that utilizes a specialized diffusion model to efficiently compress the sensing information of collaborators. We show that DiffCP can significantly reduce communication costs by 14.5-fold while maintaining the same performance as the state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2024-09-29T07:19:54Z)
• Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems [13.942103196446377]
  Collaborative Perception has shown great potential to achieve more holistic and reliable environmental perception in unmanned systems. implementing CP still faces key challenges due to the characteristics of the CP task and the dynamics of wireless channels. We propose a task-oriented wireless communication framework to jointly optimize the communication scheme and the CP procedure.
  arXiv  Detail & Related papers  (2024-06-05T09:22:19Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation [14.525705886707089]
  This paper reviews the research progress on Cooperative Perception (CP) and proposes a unified CP framework. CP is born to unlock the bottleneck of perception for driving automation. A Hierarchical CP framework is proposed, followed by a review of existing datasets and Simulators to sketch an overall landscape of CP.
  arXiv  Detail & Related papers  (2022-08-22T20:47:35Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems [10.257042901204528]
  Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures. The components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. We propose a novel Semi-a synchronous Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing.
  arXiv  Detail & Related papers  (2021-10-18T07:44:34Z)
• A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities [3.563646182996609]
  Connected and automated vehicles (CAVs) can alleviate traffic congestion, air pollution, and improve safety. In this paper, we provide a decentralized coordination framework for CAVs at a signal-free intersection to minimize travel time and improve fuel efficiency.
  arXiv  Detail & Related papers  (2020-11-05T23:30:05Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)

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.