TL;DR of L2D, the world's largest self driving dataset! Read more about L2D on the official Huggingface blog: LeRobot goes to driving school 90+ TeraBytes of multimodal data (5000+ hours of driving) from 30 cities in Germany 6x surrounding HD cameras and complete vehicle state: Speed/Heading/GPS/IMU Continuous: Gas/Brake/Steering and discrete actions: Gear/Turn Signals Environment state: Lane count, Road type (highway residential), Road surface (asphalt, cobbled, sett), Max speed limit. Environment conditions: Precipitation, Conditions (Snow, Clear, Rain), Lighting (Dawn, Day, Dusk) Designed for training end to end models conditioned on natural language instructions or future waypoints Natural language instructions. F.ex "When the light turns green, drive over the tram tracks and then through the roundabout" for each episode Future waypoints snapped to OpenStreetMap graph, aditionally rendered in birds eye view Expert (driving instructors) and student (learner drivers) policies State of the art Vision Language Models and Large Language Models are trained on open source image text corpora sourced from the internet, which spearheaded the recent acceleration of open source AI. Despite…
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