Via Sarah Rios.

Originally shared by Wayne Radinsky

“In a new automotive application, we have used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. This powerful end-to-end approach means that with minimum training data from humans, the system learns to steer, with or without lane markings, on both local roads and highways. The system can also operate in areas with unclear visual guidance such as parking lots or unpaved roads.”

“We designed the end-to-end learning system using an NVIDIA DevBox running Torch 7 for training. An NVIDIA DRIVETM PX self-driving car computer, also with Torch 7, was used to determine where to drive — while operating at 30 frames per second (FPS). The system is trained to automatically learn the internal representations of necessary processing steps, such as detecting useful road features, with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. In contrast to methods using explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously.”

https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/#.V7mmq63NpGY.twitter