# MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving arXiv: https://arxiv.org/abs/2302.13029 ยท HTML: https://mlsanity.com/paper/2302.13029/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 58 references that are in the map. - First posted on arXiv 25 Feb 2023. ## Builds on (most cited first, 25 of 25) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018) - [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022) - [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016) - [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022) - [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019) - [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020) - [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022) - [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022) - [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021) - [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019) - [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019) - [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020) - [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020) - [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020) - [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019) - [Learning to Communicate and Correct Pose Errors](https://mlsanity.com/paper/2011.05289/) (2020) - [AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving](https://mlsanity.com/paper/2112.14947/) (2021) - [DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving](https://mlsanity.com/paper/2207.07609/) (2022) - [Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning](https://mlsanity.com/paper/2012.03414/) (2020) - [VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection](https://mlsanity.com/paper/2212.07060/) (2022) - [Online V2X Scheduling for Raw-Level Cooperative Perception](https://mlsanity.com/paper/2202.06085/) (2022) - [EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks](https://mlsanity.com/paper/1709.02582/) (2017) - [Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm](https://mlsanity.com/paper/2002.08833/) (2020) - [Congestion Control in the Cellular-V2X Sidelink](https://mlsanity.com/paper/2106.04871/) (2021) ## Built on by (most cited first, 7 of 7) - [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023) - [Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems](https://mlsanity.com/paper/2406.03086/) (2024) - [Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning](https://mlsanity.com/paper/2406.17470/) (2024) - [WHALES: A Multi-Agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving](https://mlsanity.com/paper/2411.13340/) (2024) - [EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models](https://mlsanity.com/paper/2412.09782/) (2024) - [AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairments](https://mlsanity.com/paper/2605.01888/) (2026) - [Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception](https://mlsanity.com/paper/2502.10456/) (2025) ## Abstract (arXiv) Timely and reliable environment perception is fundamental to safe and efficient automated driving. However, the perception of standalone intelligence inevitably suffers from occlusions. A new paradigm, Cooperative Perception (CP), comes to the rescue by sharing sensor data from another perspective, i.e., from a cooperative vehicle (CoV). Due to the limited communication bandwidth, it is essential to schedule the most beneficial CoV, considering both the viewpoints and communication quality. Existing methods rely on the exchange of meta-information, such as visibility maps, to predict the perception gains from nearby vehicles, which induces extra communication and processing overhead. In this paper, we propose a new approach, learning while scheduling, for distributed scheduling of CP. The solution enables CoVs to predict the perception gains using past observations, leveraging the temporal continuity of perception gains. Specifically, we design a mobility-aware sensor scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB) theory to maximize the expected average perception gain. An upper bound on the expected average learning regret is proved, which matches the lower bound of any online algorithm up to a logarithmic factor. Extensive simulations are carried out on realistic traffic traces. The results show that 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. Finally, a case study on a trace of LiDAR frames qualitatively demonstrates the superiority of adaptive exploration, the key element of the MASS algorithm.