Name
Abstract | ||
|---|---|---|
An autopilot system includes several modules, and the software architecture has a variety of programs. As we all know, it is necessary that there exists one brand with a compatible sensor system till now, owing to complexity and variety of sensors before. In this paper, we apply (Robot Operating System) ROS-based distributed architecture. Deep learning methods also adopted by perception modules. Experimental results demonstrate that the system can reduce the dependence on the hardware effectively, and the sensor involved is convenient to achieve well the expected functionalities. The system adapts well to some specific driving scenes, relatively fixed and simple driving environment, such as the inner factories, bus lines, parks, highways, etc. This paper presents the case study of autopilot system based on ROS and deep learning, especially convolution neural network (CNN), from the perspective of system implementation. And we also introduce the algorithm and realization process including the core module of perception, decision, control and system management emphatically. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE 15TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) | autopilot, distributed, deep learning, ROS, perception |
Field | DocType | ISSN |
Convolutional neural network,Computer science,Convolution,Implementation,Autopilot,Artificial intelligence,Control system,Software architecture,Deep learning,Systems management,Distributed computing | Conference | 1935-4576 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Liu | 1 | 39 | 18.70 |
| Jianwei Niu | 2 | 1643 | 141.54 |
| Xin Wang | 3 | 1 | 1.37 |