Simulation for Training and Testing Intelligent Systems

Intelligent systems require sizable sets of known data for training. Developing datasets from real-world data is a time-consuming and cumbersome process. Simulation provides an alternative with key advantages: rapid data collection, automated semantic tagging, lower cost, etc. at the cost of realism. The major automotive autonomy efforts rely on simulation to supplement real-world driving with millions of miles of simulated driving. Simulators are also being used to develop and test autonomous algorithms for do-it-yourself autonomous remote-control cars, small-to-medium sized unmanned ground vehicles, and intelligent agents that exist only in virtual environments. Building and using simulated environments for training and testing intelligent systems provides many significant advantages, users must be aware of the limitations and constraints of simulated environments and carefully evaluate transfer of learning from simulated datasets to real-world performance.