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Scale claims this is the first time such data has been released with zero restrictions
A self-driving vehicle hardware manufacturer and a computer vision company have teamed up to release a free a data set for training ML models used in autonomous cars.
Shanghai-based Hesai manufactures LiDAR systems, the technology used in most self-driving vehicles.
Meanwhile Scale AI develops object recognition software used by Zoox, Lyft, Toyota, and OpenAI, among others.
PandaSet is free and licensed for academic and commercial use; the data set is based on information collected before the Covid-19 lockdown. The companies used a Chrysler Pacifica minivan featuring the forward-facing PandarGT, and a spinning LiDAR system, Pandar64, as well as wide-angle cameras and one long-focus camera.
The data was annotated with Scale's technology, and includes more than 48,000 camera images, 16,000 LiDAR sweeps, 100 scenes of 8s each, 28 annotation classes, and 37 semantic segmentation labels.
The scenes are selected from two routes in Silicon Valley: San Francisco, and El Camino Real, from Palo Alto to San Mateo. The partners claim they selected the routes to showcase complex urban driving scenarios, including steep hills, construction, dense traffic and pedestrians, and a variety of times of day and lighting conditions in the morning, afternoon, and evening.
“Machine learning is definitely a ‘garbage in, garbage out’ kind of framework - you really need high-quality data to be able to power these algorithms,” Scale AI CEO Alexandr Wang told TechCrunch.
“There’s a big need right now and a continual need for high-quality labeled data. That’s one of the biggest hurdles overcome when building self-driving systems. We want to democratize access to this data, especially at a time when a lot of the self-driving companies can’t collect it.”
Scale claims this is the first time such data has been released with zero restrictions. Previously, companies including Scale, Argo.AI, Waymo, and Cruise have shared autonomous vehicle data - but either in a highly limited form, or specifically for non-commercial research.