  Recent advancements in computer graphics technology allow more realistic
ren-dering of car driving environments. They have enabled self-driving car
simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large
amounts of synthetic data that can complement the existing real-world dataset
in training autonomous car perception. Furthermore, since self-driving car
simulators allow full control of the environment, they can generate dangerous
driving scenarios that the real-world dataset lacks such as bad weather and
accident scenarios. In this paper, we will demonstrate the effectiveness of
combining data gathered from the real world with data generated in the
simulated world to train perception systems on object detection and
localization task. We will also propose a multi-level deep learning perception
framework that aims to emulate a human learning experience in which a series of
tasks from the simple to more difficult ones are learned in a certain domain.
The autonomous car perceptron can learn from easy-to-drive scenarios to more
challenging ones customized by simulation software.
