International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 469 - 482

A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm

Authors
Guotao Xie1, xieguotao1990@126.com, Hongbo Gao2, *, ghb48@mail.tsinghua.edu.cn, Bin Huang3, lullice@163.com, Lijun Qian4, hfutqlj@163.com, Jianqiang Wang5, †, wjqlws@tsinghua.edu.cn
1Department of Automotive Engineering, Hefei University of Technology, Hefei 230009, China State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
2State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
3State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
4Department of Automotive Engineering, Hefei University of Technology, Hefei 230009, China
5State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100084, China
*

Guotao Xie and Hongbo Gao contributed equally to this work

†Corresponding author: wjqlws@tsinghua.edu.cn
Corresponding Author
Received 25 July 2017, Accepted 22 December 2017, Available Online 1 January 2018.
DOI
10.2991/ijcis.11.1.35How to use a DOI?
Keywords
Automated Vehicle; Advanced Driver Assistance System; Driving Behavior Awareness; Dynamic Bayesian Network; Distributed Genetic Algorithm
Abstract

It is necessary for automated vehicles (AVs) and advanced driver assistance systems (ADASs) to have a better understanding of the traffic environment including driving behaviors. This study aims to build a driving behavior awareness (DBA) model that can infer driving behaviors such as lane change. In this study, a dynamic Bayesian network DBA model is proposed, which includes three layers, namely, the observation, hidden and behavior layer. To enhance the performance of the DBA model, the network structure is optimized by employing a distributed genetic algorithm (GA). Using naturalistic driving data in Beijing, the comparison between the optimized model and other non-optimized models such as the hidden Markov model (HMM) and HMM with a mixture of Gaussian outputs (GM-HMM) indicates that the optimized model could estimate driving behaviors earlier and more accurately.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
469 - 482
Publication Date
2018/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.35How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Guotao Xie
AU  - Hongbo Gao
AU  - Bin Huang
AU  - Lijun Qian
AU  - Jianqiang Wang
PY  - 2018
DA  - 2018/01/01
TI  - A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 469
EP  - 482
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.35
DO  - 10.2991/ijcis.11.1.35
ID  - Xie2018
ER  -