  Autonomous vehicles need safe development and testing environments. Many
traffic scenarios are such that they cannot be tested in the real world. We see
hybrid photorealistic simulation as a viable tool for developing AI (artificial
intelligence) software for autonomous driving. We present a machine learning
environment for detecting autonomous vehicle corner case behavior. Our
environment is based on connecting the CARLA simulation software to TensorFlow
machine learning framework and custom AI client software. The AI client
software receives data from a simulated world via virtual sensors and
transforms the data into information using machine learning models. The AI
clients control vehicles in the simulated world. Our environment monitors the
state assumed by the vehicle AIs to the ground truth state derived from the
simulation model. Our system can search for corner cases where the vehicle AI
is unable to correctly understand the situation. In our paper, we present the
overall hybrid simulator architecture and compare different configurations. We
present performance measurements from real setups, and outline the main
parameters affecting the hybrid simulator performance.
