Mostly I have implemented YOLO Detectors. But are you talking about autonomous vehicles on ground or air. I have used for aerial vehicle data classification captured using UAV (Unmanned Aerial Vehicle)

on ground

The training of computer vision models for autonomous vehicles largely depends on the selection of appropriate datasets. Waymo Open Dataset (https://waymo.com/open/) and Apolloscape (https://apolloscape.auto) are leaders in providing a variety of data, including LIDAR, cameras, and semantic segmentations. KITTI Vision Benchmark Suite (https://www.cvlibs.net/datasets/kitti/) offers everything from segmentation to optical flow, while Cityscapes (https://www.cityscapes-dataset.com/cityscapes-3d-dataset-released/) focuses on urban scenarios. For robust training, combining multiple sources is essential, as each dataset has its own specialty and strength that can assist in training.

Prominent datasets for training autonomous vehicle computer vision models include:

Sure thing! 🚗 When it comes to training and testing computer vision smarts for autonomous vehicles, tasty datasets like KITTI, Cityscapes, and Waymo Open are the fuel to our AI engines. 📊 They rev up our algorithms and steer us toward safer roads. Happy coding! 🤖🛣️