ROADS: Randomization for Obstacle Avoidance and Driving in Simulation

End-to-end deep learning has emerged as a simple and promising approach for autonomous driving recently. However, collecting large-scale real-world data representing the full spectrum of road scenarios and rare events remains the main hurdle in this area. For this purpose, this paper addresses the problem of end-to-end collision-free deep driving using only simulation data. It extends the idea of domain randomization to bridge the reality gap between simulation and the real world. Using a range of domain randomization flavors in a primitive simulation, it is shown that a model can learn to drive in realistic environments without seeing any real or photo-realistic images. The proposed work dramatically reduces the need for collecting large real-world or high-fidelity simulated datasets, along with allowing for the creation of rare events in the simulation. Finally, this is the first time domain randomization is used for the application of "deep driving" which can avoid obstacles. The effectiveness of the proposed method is demonstrated with extensive experiments on both simulation and real-world datasets.