{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/airsim-high-fidelity-visual-and-physical","title":"AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles","arxiv_id":"1705.05065","date":"2017-05-15","proceeding":null,"authors":["Shital Shah","Debadeepta Dey","Chris Lovett","Ashish Kapoor"],"abstract":"Developing and testing algorithms for autonomous vehicles in real world is an\nexpensive and time consuming process. Also, in order to utilize recent advances\nin machine intelligence and deep learning we need to collect a large amount of\nannotated training data in a variety of conditions and environments. We present\na new simulator built on Unreal Engine that offers physically and visually\nrealistic simulations for both of these goals. Our simulator includes a physics\nengine that can operate at a high frequency for real-time hardware-in-the-loop\n(HITL) simulations with support for popular protocols (e.g. MavLink). The\nsimulator is designed from the ground up to be extensible to accommodate new\ntypes of vehicles, hardware platforms and software protocols. In addition, the\nmodular design enables various components to be easily usable independently in\nother projects. 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