Abstract

This paper is a review of different methods and systems for monitoring and inventory of road signs and markings across Europe. The motorization rate has grown rapidly in recent years and traffic increases. Moreover, autonomous vehicles are being developed and it is expected that vehicles with conditional automation are driving on motorways from 2019. One of the greatest challenges for advanced driver assistant systems (ADAS) is Environment detection, perception and prediction – the capability to perceive a vehicle’s environment using sensor ADAS systems use a variety of sensors and cameras to observe its surroundings, to locate itself on the road and help it to drive safely. Cameras e.g. detect road signs and Lidar and Radar sensors help to detect the edges of roads and identify lane markings. These developments have implications for road operators too. Road marking and road signs need to be consistently inventoried; their condition needs to be in a reliable state to allow a trusting use by humans and automated cars. Measurement of surface characteristics as skid resistance, evenness, texture and deterioration with high-speed systems and emerging equipment are well established, while others for monitoring and inventory of road signs and markings are not. So far, very little attention has been paid to the condition of these two road assets. The purpose of this paper is to review different methods and devices for inventory and condition data collection of road signs and markings across Europe. Measurement systems and experience are reported and the gaps in the existing methods were identified. In addition, the necessary inventory data for project level and network level are determined. Taken together, these results suggest methods and systems for collecting condition and inventory data for road signs and markings.


Original document

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

https://zenodo.org/record/1320920 under the license https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
http://dx.doi.org/10.5281/zenodo.1320919 under the license https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode


DOIS: 10.5281/zenodo.1320919 10.5281/zenodo.1320920

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.1320919
Licence: Other

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