Paper Info
Title
End-to-End Navigation with Branch Turning Support Using Convolutional Neural Network
Abstract
End-to-end navigation refers to methods of generating control signals for mobile autonomous devices directly from external sensors, which is an area gaining attention within the autonomous driving research community. Previous autonomous driving research has mostly been focused on keeping moving vehicles within their lanes, but reliable navigation along other trajectories, including branching off onto other routes, has not yet been achieved. In this study we propose a deep learning system for end-to-end navigation which would allow an autonomous vehicle to turn at intersections. Our system's inputs include camera images and directions to a target, while the outputs are the steering control signals needed to direct the vehicle. We validate the system's performance by conducting experiments involving three different driving scenarios: short, indoor trajectories containing a single branching turn; long, outdoor trajectories containing many branching turns; and long, outdoor trajectories which were not included during training. Our end-to-end navigation system allowed an autonomous robot to successfully follow outdoor trajectories with right and left turns, including those which were not part of the training course.
Year
DOI
Venue
2018
10.1109/ROBIO.2018.8665079
2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Keywords
Field
DocType
Navigation,Trajectory,Training,Cameras,Robots,Deep learning,Data models
Data modeling,Convolutional neural network,End-to-end principle,Navigation system,Control engineering,Artificial intelligence,Deep learning,Engineering,Robot,Autonomous robot,Trajectory
Conference
ISBN
Citations 
PageRank 
978-1-7281-0377-8
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shunya Seiya100.68
Alexander Carballo2738.31
Eijiro Takeuchi324226.05
Chiyomi Miyajima434545.71
Kazuya Takeda51301195.60