# The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

arXiv: https://arxiv.org/abs/2301.07325 · HTML: https://mlsanity.com/paper/2301.07325/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2023.

## Built on by (most cited first, 27 of 27)

- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints](https://mlsanity.com/paper/2302.13933/) (2023)
- [Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles](https://mlsanity.com/paper/2402.07635/) (2024)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception](https://mlsanity.com/paper/2411.10962/) (2024)
- [S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality](https://mlsanity.com/paper/2307.07935/) (2023)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems](https://mlsanity.com/paper/2304.01534/) (2023)
- [MACP: Efficient Model Adaptation for Cooperative Perception](https://mlsanity.com/paper/2310.16870/) (2023)
- [V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions](https://mlsanity.com/paper/2403.11371/) (2024)
- [Generating Evidential BEV Maps in Continuous Driving Space](https://mlsanity.com/paper/2302.02928/) (2023)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V](https://mlsanity.com/paper/2309.15572/) (2023)
- [Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources](https://mlsanity.com/paper/2402.04273/) (2024)
- [Learn from Foundation Model: Fruit Detection Model without Manual Annotation](https://mlsanity.com/paper/2411.16196/) (2024)
- [V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2503.15435/) (2025)
- [AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations](https://mlsanity.com/paper/2412.06142/) (2024)
- [Self-supervised Adaptive Weighting for Cooperative Perception in V2V Communications](https://mlsanity.com/paper/2312.10342/) (2023)
- [AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding](https://mlsanity.com/paper/2402.17521/) (2024)
- [AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairments](https://mlsanity.com/paper/2605.01888/) (2026)
- [Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2607.01928/) (2026)
- [Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving](https://mlsanity.com/paper/2305.06966/) (2023)
- [OpenConvoy: Universal Platform for Real-World Testing of Cooperative Driving Systems](https://mlsanity.com/paper/2405.18600/) (2024)
- [An Agile Adaptation Method for Multi-mode Vehicle Communication Networks](https://mlsanity.com/paper/2408.01429/) (2024)
- [AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software](https://mlsanity.com/paper/2505.06749/) (2025)

## Abstract (arXiv)

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation.