ai4ce/V2X-Sim
[RA-L2022] V2X-Sim Dataset and Benchmark
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.
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Last pushed
Jan 24, 2025
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