Driver Modeling Based on Vehicular Sensing Data

DOI

10.2991/acaai-18.2018.32How to use a DOI?

Keywords

driving behavior models; vehicular sensing data; machine learning; driver identification

Abstract

In the past few years, the automotive electronics and sensing technologies have developed rapidly. Today, the status of most of the sub-systems in a running vehicle can be accurately monitored. This process produces a huge amount of data. Extracting the potential value of such data, to for instance support developing advanced vehicle diagnosis and active safety applications, has attracted tremendous attentions in both academia and industry. Considering that the sensing data, if sampled with sufficiently high frequency, can accurately represent how a driver maneuvers a vehicle, this paper investigates using the vehicular sensing data to exploit drivers' behaviors in different traffic scenarios. We apply machine learning techniques to construct driving behavior models, and discuss their applications in driver identification.

Copyright

© 2018, 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  - Zhuowen Wang
AU  - Fuqiang Liu
AU  - Xinhong Wang
AU  - Yuyan Du
PY  - 2018/03
DA  - 2018/03
TI  - Driver Modeling Based on Vehicular Sensing Data
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 137
EP  - 141
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.32
DO  - 10.2991/acaai-18.2018.32
ID  - Wang2018/03
ER  -