Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.

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This paper describes the design and implementation of the open-source tool $$\textsc {Coyote} $$ C O Y O T E for testing concurrent programs written in the $$\textsc {C}{} \texttt {\#} $$ C # language. $$\textsc {Coyote} $$ C O Y O T E provides algorithmic capabilities to explore the state-space of interleavings of a concurrent program, with deterministic repro for any bug that it finds. $$\textsc {Coyote} $$ C O Y O T E encapsulates multiple ideas from the research community to offer state-of-the-art testing for $$\textsc {C}{} \texttt {\#} $$ C # programs, as well as an efficiently engineered implementation that has been shown robust enough to support industrial use.

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