# Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning

arXiv: https://arxiv.org/abs/2012.03414 · HTML: https://mlsanity.com/paper/2012.03414/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Collaborative Perception for Autonomous Driving: Current Status and Future Trend](https://mlsanity.com/paper/2208.10371/) (2022)
- [MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving](https://mlsanity.com/paper/2302.13029/) (2023)
- [Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels](https://mlsanity.com/paper/2106.00995/) (2021)
- [A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration](https://mlsanity.com/paper/2312.16142/) (2023)
- [An Advantage-based Optimization Method for Reinforcement Learning in Large Action Space](https://mlsanity.com/paper/2412.12605/) (2024)
- [SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services](https://mlsanity.com/paper/2508.06243/) (2025)
- [Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption](https://mlsanity.com/paper/2607.19146/) (2026)

## Abstract (arXiv)

Cooperative perception plays a vital role in extending a vehicle's sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to address the following fundamental question: What sensory data needs to be shared?, at which resolution?, and with which vehicles? To answer this question, in this paper, a novel framework is proposed to allow reinforcement learning (RL)-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) by utilizing a quadtree-based point cloud compression mechanism. Furthermore, a federated RL approach is introduced in order to speed up the training process across vehicles. Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information. The results also show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.