Traffic Scenes Classification for Self-driving Car

DOI

10.2991/icsmim-15.2016.25How to use a DOI?

Keywords

Traffic Scenes Classification, Self-driving Car, Road Network Definition

Abstract

In this paper, machine learning model is applied to label the attributes of traffic scenes automatically in the RNDF (Road Network Definition File), which is used in the self-driving car. The “gist” features extracted from one image of the video stream are used as the model’s input. In the experiments, the test model is SVM (Support Vector Machine) and the training and test samples are from the FM2 database. The experiments verify the feasibility and effectiveness of this method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongbo Lv
AU  - Xiaolin Zhuang
AU  - Huifang Cao
PY  - 2016/01
DA  - 2016/01
TI  - Traffic Scenes Classification for Self-driving Car
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 130
EP  - 133
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.25
DO  - 10.2991/icsmim-15.2016.25
ID  - Lv2016/01
ER  -