Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception

• URL: http://arxiv.org/abs/2601.04542v1
• Date: Thu, 08 Jan 2026 03:16:00 GMT
• Title: Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception
• Authors: Mengmeng Zhu, Yuxuan Sun, Yukuan Jia, Wei Chen, Bo Ai, Sheng Zhou,
• Abstract summary: Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities.<n>Due to the dynamic nature of the environment, the timeliness of the transmitted information is critical to perception performance.<n>This work studies the dynamic scheduling problem in a multi-region CP scenario, and presents a Timeliness-Aware Multi-region Prioritized (TAMP) scheduling algorithm.
• Score: 19.430782700000158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities. It involves the sharing and fusion of information among sensors to overcome the limitations of individual perception, such as blind spots and range limitations. However, CP faces two primary challenges. First, due to the dynamic nature of the environment, the timeliness of the transmitted information is critical to perception performance. Second, with limited computational power at the sensors and constrained wireless bandwidth, the communication volume must be carefully designed to ensure feature representations are both effective and sufficient. This work studies the dynamic scheduling problem in a multi-region CP scenario, and presents a Timeliness-Aware Multi-region Prioritized (TAMP) scheduling algorithm to trade-off perception accuracy and communication resource usage. Timeliness reflects the utility of information that decays as time elapses, which is manifested by the perception performance in CP tasks. We propose an empirical penalty function that maps the joint impact of Age of Information (AoI) and communication volume to perception performance. Aiming to minimize this timeliness-oriented penalty in the long-term, and recognizing that scheduling decisions have a cumulative effect on subsequent system states, we propose the TAMP scheduling algorithm. TAMP is a Lyapunov-based optimization policy that decomposes the long-term average objective into a per-slot prioritization problem, balancing the scheduling worth against resource cost. We validate our algorithm in both intersection and corridor scenarios with the real-world Roadside Cooperative perception (RCooper) dataset. Extensive simulations demonstrate that TAMP outperforms the best-performing baseline, achieving an Average Precision (AP) improvement of up to 27% across various configurations.

Related papers

• Priority-Aware Multi-Robot Coverage Path Planning [29.15118436759072]
  We introduce the Priority-Aware MCPP problem, where a subset of the environment is designated as prioritized zones with associated weights.<n>The goal is to minimize, in lexicographic order, the total priority-weighted latency of zone coverage and the overall makespan.<n> Experiments across diverse scenarios demonstrate that our method significantly reduces priority-weighted latency compared to standard MCPP baselines.
  arXiv  Detail & Related papers  (2026-01-02T05:45:15Z)
• Joint Channel Estimation and Computation Offloading in Fluid Antenna-assisted MEC Networks [81.36647816787713]
  We propose an FA-assisted offloading framework to minimize the delay of channel estimation.<n>We show that the proposed system significantly reduces the accuracy under efficient communication.
  arXiv  Detail & Related papers  (2025-09-16T08:48:44Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Pull-Based Query Scheduling for Goal-Oriented Semantic Communication [14.787190731074322]
  This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems.<n>We introduce a grade of effectiveness (GoE) metric and integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis.<n>We propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms.
  arXiv  Detail & Related papers  (2025-03-09T18:51:14Z)
• 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)
• Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm [87.47506806135746]
  In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off.
  arXiv  Detail & Related papers  (2024-04-01T00:21:11Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving [19.66714697653504]
  A new paradigm, Cooperative Perception (CP), comes to the rescue by sharing sensor data from a cooperative vehicle (CoV) Existing methods rely on the exchange of meta-information, such as visibility maps, to predict the perception gains from nearby vehicles. We propose a new approach, learning while scheduling, for distributed scheduling of CP. The proposed MASS algorithm achieves the best average perception gain and improves recall by up to 4.2 percentage points compared to other learning-based algorithms.
  arXiv  Detail & Related papers  (2023-02-25T09:03:05Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Online V2X Scheduling for Raw-Level Cooperative Perception [21.099819062731463]
  Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. We present a model of raw-level cooperative perception and formulate the energy minimization problem of sensor sharing scheduling. We propose an online learning-based algorithm with logarithmic performance loss, achieving a decent trade-off between exploration and exploitation.
  arXiv  Detail & Related papers  (2022-02-12T15:16:45Z)
• Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling [57.271494741212166]
  This paper formulates and solves the communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
  arXiv  Detail & Related papers  (2021-07-24T11:39:17Z)

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.