Bernini et al 2013a - Revision history

Scipediacontent at 13:23, 21 January 2021

2021-01-21T13:23:09Z

Scipediacontent: Scipediacontent moved page Draft Content 965408936 to Bernini et al 2013a

2020-10-14T11:59:10Z

Scipediacontent moved page Draft Content 965408936 to Bernini et al 2013a

Scipediacontent: Created page with " == Abstract == To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelli..."

2020-10-14T11:59:08Z

Created page with " == Abstract == To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelli..."

New page

== Abstract ==

To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelligent vehicle, a tool that can classify the scenarios which the vehicle moves in, is needed. This article presents a comparison of three classification techniques (PCA, ANN and SVM) to obtain a fast and robust scene classifier based only on images. The systems presented in this paper have been trained on three different categories of traffic scenarios: urban, highway, and rural, on a total of more than 23 hours of driving in different countries.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_965408936-beopen433-1758-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1007/978-3-642-41181-6_59 http://dx.doi.org/10.1007/978-3-642-41181-6_59]

* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-41181-6_59.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-41181-6_59.pdf]

@@ Line 2: / Line 2: @@
 == Abstract ==
-To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelligent vehicle, a tool that can classify the scenarios which the vehicle moves in, is needed. This article presents a comparison of three classification techniques (PCA, ANN and SVM) to obtain a fast and robust scene classifier based only on images. The systems presented in this paper have been trained on three different categories of traffic scenarios: urban, highway, and rural, on a total of more than 23 hours of driving in different countries.
+To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelligent vehicle, a tool that can classify the scenarios which the vehicle moves in, is needed.
-−
-Document type: Part of book or chapter of book
-−
-== Full document ==
-<pdf>Media:Draft_Content_965408936-beopen433-1758-document.pdf</pdf>
@@ Line 17: / Line 12: @@
 * [https://link.springer.com/content/pdf/10.1007%2F978-3-642-41181-6_59.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-41181-6_59.pdf]

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