Cited by 2,254Aerospace Engineering and Control SystemsSimulation and Modeling ApplicationsReinforcement Learning in Robotics

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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Shah, Shital, Dey, Debadeepta, Lovett, Chris, Kapoor, Ashish·2018·Springer Proceedings in Advanced Robotics

DOI 10.1007/978-3-319-67361-5_40

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Authors: Shah, S., Dey, D., Lovett, C., & Kapoor, A.
Affiliations: Microsoft (United States) (US)
Corresponding author: Shah, Shital
ORCID: Shah, Shital
Kapoor, Ashish
Venue: Springer Proceedings in Advanced Robotics
Publisher: Springer International Publishing
Published: 2017-11-03
Pages: 621-635
ISSN: 2511-1256 (ISSN-L)
2511-1264
DOI: 10.1007/978-3-319-67361-5_40
OpenAlex: W2615547864
Cited by: 2,254
References: 18
Citations by year: 2026: 199 · 2025: 345 · 2024: 317 · 2023: 330 · 2022: 298 · 2021: 298 · 2020: 219 · 2019: 165 · 2018: 75 · 2017: 7
FWCI: 406.55
Type: book chapter
Language: en
Open access: closed
Licence: www.springer.com/tdm (unspecified)
Locations: Springer proceedings in advanced robotics (publishedVersion)
Topics: Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords: AirSim, autonomous vehicles, Unreal Engine, physics simulation, visual simulation, hardware-in-the-loop, MAVLink, quadrotor, training data collection, deep learning, real-time simulation

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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

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