V2X-Sim, short for vehicle-to-everything simulation, is the a synthetic collaborative perception dataset in autonomous driving developed by AI4CE Lab at NYU and MediaBrain Group at SJTU to facilitate collaborative perception between multiple vehicles and roadside infrastructure. Data is collected from both roadside and vehicles when they are presented near the same intersection. With information from both the roadside infrastructure and vehicles, the dataset aims to encourage research on collaborative perception tasks.
Although not collected from the real world, highly realistic traffic simulation software is used to ensure the representativeness of the dataset compared to real-world driving scenarios. To be more exact, the traffic flow of the recording files is managed by CARLA-SUMO co-simulation, and three town maps from CARLA are currently used to increase the diversity of the dataset.
Here is a tutorial showing how to load the dataset: https://ai4ce.github.io/V2X-Sim/tutorial.html
Variants: V2X-SIM
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| 3D Object Detection | QUEST | QUEST: Query Stream for Practical … | 2023-08-03 |
| 3D Object Detection | Where2comm | Where2comm: Communication-Efficient Collaborative Perception via … | 2022-09-26 |
| 3D Object Detection | V2X-ViT | V2X-ViT: Vehicle-to-Everything Cooperative Perception with … | 2022-03-20 |
| 3D Object Detection | DiscoNet | Learning Distilled Collaboration Graph for … | 2021-11-01 |
| 3D Object Detection | V2VNet | V2VNet: Vehicle-to-Vehicle Communication for Joint … | 2020-08-17 |
Recent papers with results on this dataset: