# Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles

arXiv: https://arxiv.org/abs/2004.10927 · HTML: https://mlsanity.com/paper/2004.10927/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (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)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2303.11888/) (2023)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models](https://mlsanity.com/paper/2412.09782/) (2024)
- [Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown](https://mlsanity.com/paper/2301.01250/) (2022)
- [Towards Secure and Efficient Data Scheduling for Vehicular Social Networks](https://mlsanity.com/paper/2407.00141/) (2024)
- [Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication](https://mlsanity.com/paper/2501.17329/) (2025)

## Abstract (arXiv)

Sensor-based perception on vehicles are becoming prevalent and important to enhance the road safety. Autonomous driving systems use cameras, LiDAR, and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using the deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular communication networks and enhances the communication reliability. To design, test, and verify the cooperative perception scheme, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform, which integrates three software components: traffic simulator, vehicle simulator, and object classifier. We evaluate that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.