Learning a deep neural net policy for end-to-end control of autonomous vehicles
Viktor Rausch, Andreas Hansen, Eugen Solowjow, Chang Liu, Edwin J. Kreuzer, J. Karl Hedrick · 2017
Deep neural networks are frequently used for computer vision, speech recognition and text processing. The reason is their ability to regress highly nonlinear functions. We present an end-to-end controller for steering autonomous vehicles based on a convolutional neural network (CNN). The deployed framework does not require explicit hand-engineered algorithms for lane detection, object detection or path planning. The trained neural net directly maps pixel data from a front-facing camera to steering commands and does not require any other sensors. We compare the controller performance with the steering behavior of a human driver.