Driving Risk Classification based on Experts Evaluation

被引：20

作者：

Siordia, Oscar S. ^{[1
]}

Martin de Diego, Isaac ^{[1
]}

Conde, Cristina ^{[1
]}

Reyes, Gerardo

Cabello, Enrique ^{[1
]}

机构：

[1] Univ Rey Juan Carlos, Madrid, Spain

来源：

2010 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2010年

关键词：

D O I：

10.1109/IVS.2010.5548130

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A novel multidisciplinary system for the automatic driving risk level classification is presented. The data considered involves the three basic traffic safety elements (driver, road, and vehicle), as well as knowledge from traffic experts. The driving experiments were conducted in a truck cabin simulator handled by a professional driver, considering the most common real-world enviroments. Each traffic expert evaluate the driving risk on a 0 to 100 visual analogue scale. The driver, road and vehicle information was used to train five different data mining algorithms in order to predict the driving risk level. The benefits of the completeness of the data considered in our system are presented and discussed.

引用

页码：1098 / 1103

页数：6

共 18 条

[1]

[Anonymous], 2001, The role of driver Distraction in traffic crashes, DOI 10.1037/e363942004-001

[2]

[Anonymous], 1984, OLSHEN STONE CLASSIF, DOI 10.2307/2530946

[3]

[Anonymous], 2006, 100 CAR NATURALISTIC

[4]

[Anonymous], P ACM SIGCHI 2005

[5]

Brazalez A., 2008, P 8 C ESP SIST INT T

[6] Training ν-support vector classifiers:: Theory and algorithms [J].

Chang, CC ;

Lin, CJ .

NEURAL COMPUTATION, 2001, 13 (09) :2119-2147

[7]

Cohen W. W., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning, P115

[8]

Cork R.C., 2004, INT J ANESTHESIOLOGY, V1

[9] Biometric identification using driving behavioral signals [J].

Igarashi, K ;

Miyajima, C ;

Itou, K ;

Takeda, K ;

Itakura, F ;

Abut, H .

2004 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXP (ICME), VOLS 1-3, 2004, :65-68

[10]

Imamura T., 2007, SMC, P978

← 1 2 →