ai4ce/V2X-Sim

[RA-L2022] V2X-Sim Dataset and Benchmark

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This project provides a comprehensive simulated dataset for developing and testing autonomous driving systems that use vehicle-to-everything (V2X) communication. It includes multi-modal sensor data from both roadside units and multiple vehicles, along with diverse ground truths. Engineers and researchers in autonomous driving can use this data to train and benchmark perception algorithms for tasks like object detection, tracking, and segmentation.

138 stars. No commits in the last 6 months.

Use this if you are developing or evaluating collaborative perception systems for autonomous vehicles and need a rich, multi-agent, multi-modal dataset to train and benchmark your algorithms.

Not ideal if you are looking for real-world driving data from physical vehicles or a tool for general-purpose computer vision tasks unrelated to autonomous driving.

autonomous-driving vehicle-to-everything-communication perception-systems sensor-fusion robotics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 16 / 25

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Stars

138

Forks

18

Language

License

Apache-2.0

Last pushed

Jan 24, 2025

Commits (30d)

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