The NVIDIA PilotNet Experiments
Abstract
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway. This system represents a departure from the classical approach for self-driving in which the process is manually decomposed into a series of modules, each performing a different task. In PilotNet, on the other hand, a single deep neural network (DNN) takes pixels as input and produces a desired vehicle trajectory as output; there are no distinct internal modules connected by human-designed interfaces. We believe that handcrafted interfaces ultimately limit performance by restricting information flow through the system and that a learned approach, in combination with other artificial intelligence systems that add redundancy, will lead to better overall performing systems. We continue to conduct research toward that goal. This document describes the PilotNet lane-keeping effort, carried out over the past five years by our NVIDIA PilotNet group in Holmdel, New Jersey. Here we present a snapshot of system status in mid-2020 and highlight some of the work done by the PilotNet group.
Keywords
Cite
@article{arxiv.2010.08776,
title = {The NVIDIA PilotNet Experiments},
author = {Mariusz Bojarski and Chenyi Chen and Joyjit Daw and Alperen Değirmenci and Joya Deri and Bernhard Firner and Beat Flepp and Sachin Gogri and Jesse Hong and Lawrence Jackel and Zhenhua Jia and BJ Lee and Bo Liu and Fei Liu and Urs Muller and Samuel Payne and Nischal Kota Nagendra Prasad and Artem Provodin and John Roach and Timur Rvachov and Neha Tadimeti and Jesper van Engelen and Haiguang Wen and Eric Yang and Zongyi Yang},
journal= {arXiv preprint arXiv:2010.08776},
year = {2020}
}