A Hybrid Vision-Map Method for Urban Road Detection

المؤلفون المشاركون

Fernández, Carlos
Fernández-Llorca, David
Sotelo, Miguel A.

المصدر

Journal of Advanced Transportation

العدد

المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-21، 21ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2017-10-30

دولة النشر

مصر

عدد الصفحات

21

التخصصات الرئيسية

هندسة مدنية

الملخص EN

A hybrid vision-map system is presented to solve the road detection problem in urban scenarios.

The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness.

The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs.

Deep learning approaches make the system hard-coupled to the training set.

Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Fernández, Carlos& Fernández-Llorca, David& Sotelo, Miguel A.. 2017. A Hybrid Vision-Map Method for Urban Road Detection. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1170919

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Fernández, Carlos…[et al.]. A Hybrid Vision-Map Method for Urban Road Detection. Journal of Advanced Transportation No. 2017 (2017), pp.1-21.
https://search.emarefa.net/detail/BIM-1170919

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Fernández, Carlos& Fernández-Llorca, David& Sotelo, Miguel A.. A Hybrid Vision-Map Method for Urban Road Detection. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1170919

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1170919