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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
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“In recent years, simulators have been progressing towards more realistic scenarios and physics engines: Airsim [13], CARLA [14], RotorS [15], [16], and others [17].”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [13]
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Metadata
- Authors
- Shah, S., Dey, D., Lovett, C., & Kapoor, A.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Shah, Shital
- Venue
- Springer Proceedings in Advanced Robotics
- Publisher
- Springer International Publishing
- Published
- 2017-11-03
- Pages
- 621-635
- ISSN
- 2511-1256 (ISSN-L)
- 2511-1264
- 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
- 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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