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

arXiv: https://arxiv.org/abs/2601.04542 · HTML: https://mlsanity.com/paper/2601.04542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow](https://mlsanity.com/paper/2309.16940/) (2023)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2403.10145/) (2024)
- [S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation](https://mlsanity.com/paper/2505.24139/) (2025)
- [R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications](https://mlsanity.com/paper/2410.04168/) (2024)
- [Pragmatic Communication in Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2401.12694/) (2024)
- [Urgency of Information for Context-Aware Timely Status Updates in Remote Control Systems](https://mlsanity.com/paper/2002.07987/) (2020)
- [Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems](https://mlsanity.com/paper/2406.03086/) (2024)
- [Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection](https://mlsanity.com/paper/2503.17175/) (2025)
- [Timely Monitoring of Dynamic Sources with Observations from Multiple Wireless Sensors](https://mlsanity.com/paper/2012.12179/) (2020)
- [Timeliness of Information for Computation-intensive Status Updates in Task-oriented Communications](https://mlsanity.com/paper/2209.13259/) (2022)
- [Optimizing Task-Specific Timeliness With Edge-Assisted Scheduling for Status Update](https://mlsanity.com/paper/2310.18895/) (2023)
- [Minimum Age of Information in the Internet of Things with Non-uniform Status Packet Sizes](https://mlsanity.com/paper/1901.07069/) (2019)
- [Towards Timely Video Analytics Services at the Network Edge](https://mlsanity.com/paper/2406.14820/) (2024)
- [C-MASS: Combinatorial Mobility-Aware Sensor Scheduling for Collaborative Perception with Second-Order Topology Approximation](https://mlsanity.com/paper/2407.00412/) (2024)
- [Coded Computation across Shared Heterogeneous Workers with Communication Delay](https://mlsanity.com/paper/2109.11246/) (2021)

## Abstract (arXiv)

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.