The NVIDIA PilotNet Experiments
Bojarski, Mariusz, Chen, Chenyi, Daw, Joyjit, Değirmenci, Alperen, Deri, Joya, Firner, Bernhard, Flepp, Beat, Gogri, Sachin, Hong, Jesse, Jackel, Lawrence, Jia, Zhenhua, Lee, BJ, Liu, Bo, Liu, Fei, Muller, Urs, Payne, Samuel, Prasad, Nischal Kota Nagendra, Provodin, Artem, Roach, John, Rvachov, Timur, Tadimeti, Neha, van Engelen, Jesper, Wen, Haiguang, Yang, Eric, Yang, Zongyi
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Oct-17-2020
- Country:
- Asia > Japan (0.04)
- Europe > Italy
- Sardinia (0.04)
- North America > United States
- California
- Los Angeles County > Los Angeles (0.04)
- San Bernardino County > Barstow (0.04)
- Nevada > Clark County
- Las Vegas (0.04)
- New Jersey > Monmouth County (0.04)
- North Carolina (0.04)
- California
- Genre:
- Research Report (0.82)
- Industry:
- Automobiles & Trucks (1.00)
- Information Technology > Hardware (0.82)
- Transportation
- Ground > Road (1.00)
- Infrastructure & Services (0.93)
- Passenger (1.00)
- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning > Neural Networks
- Deep Learning (0.66)
- Representation & Reasoning (0.94)
- Robots > Autonomous Vehicles (1.00)
- Vision (1.00)
- Machine Learning > Neural Networks
- Data Science > Data Quality (0.93)
- Sensing and Signal Processing > Image Processing (1.00)
- Artificial Intelligence
- Information Technology