Using Cart to Segment Road Images

  • The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. The CART algorithm (Classification and Regression Trees) provided a machine learning boost to find road while The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. The CART algorithm (Classification and Regression Trees) provided a machine learning boost to find road while at the same time measuring when that road could not be distinguished from surrounding desert.show moreshow less

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Metadaten
Author:Bob Davies, Rainer Lienhart Author-IDgnd/120439034
URN:urn:nbn:de:bvb:384-opus4-1242
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2005-18)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Release Date:2006/06/06
Tag:CART; OpenCV; autonomously driving cars; computer vision; image processing; machine learning
GND-Keyword:Maschinelles Lernen; Maschinelles Sehen; Straßenverkehr
Institutes:Fakultät für Angewandte Informatik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik

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