Developing a safe and reliable autonomous driving system is one of the biggest challenges today. City roads are complex with different obstacles, weather conditions, and unexpected scenarios. We have released the 42dot Open Dataset to solve these complexities and to contribute toward the advancement of autonomous technology. By sharing data, we hope researchers and engineers will join our journey to building a reliable autonomous driving ecosystem. Together we can implement a safer and optimized autonomous mobility system on our roads for everyone.
The SDLane dataset is a novel lane marking dataset for autonomous driving. We provide high resolution images of 1920 X 1208 pixels which capture challenging scenarios in highways and urban areas. For each scene, we manually annotated the 2D lane geometry of all visible lane markings on the road.
The MCMOT dataset contains data of diverse moving objects collected from multiple cameras. There are bounding boxes and polygon boxes that distinguish the different sides of the moving objects. The moving objects are identified with unique IDs.
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