Training, testing, and verifying autonomous machines using simulated environments

A method comprising:

• obtaining virtual sensor data representing a simulated environment perceived by at least one virtual sensor of a virtual representation of an autonomous or semi-autonomous machine within the simulated environment, the virtual sensor data corresponding to a first data format;
• generating, based at least on the virtual sensor data corresponding to the first data format, encoded sensor data by encoding the virtual sensor data using a second data format that is associated with at least one of one or more machine learning models or a hardware-in-the-loop (HIL) device that includes hardware corresponding to the autonomous or semi-autonomous machine;
• determining, using the HIL device and based at least on the one or more machine learning models, at least one operation for the virtual representation based at least on the encoded sensor data;
• and
• transmitting operative data representing the at least one operation of the virtual representation.

A computing device comprising one or more processing units to:

• receive, from one or more computing devices, virtual sensor data representing a simulated environment perceived using at least one virtual sensor of a virtual representation of an autonomous or semi-autonomous machine within the simulated environment, the computing device being part of a hardware-in-the-loop (HIL) device that includes hardware for installation in the autonomous or semi-autonomous machine;
• determine, using one or more machine learning models, at least one operation for the virtual representation based at least on the virtual sensor data;
• and
• transmit, to at least one computing device of the one or more computing devices, operative data representing the at least one operation of the virtual representation.