Search results for "K4011-4343"

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Traffic Flow Variables Estimation: An Automated Procedure Based on Moving Observer Method. Potential Application for Autonomous Vehicles

2019

Abstract The estimation of traffic flow variables (flow, space mean speed and density) plays a fundamental role in highways planning and designing, as well as in traffic control strategies. Moving Observer Method (MOM) allows traffic surveys in a road, or in a road network. This paper proposes a novel automated procedure, called MOM-AP based on Moving Observer Method and Digital Image Processing (DIP) Technique able to automatically detect (without human observers) and calculate flow q, space mean speed vs and density k in case of stationary and homogeneous traffic conditions. In order to evaluate how reliable is the MOM-AP, an experiment has been carried out in a segment of one two-lane si…

050210 logistics & transportationComputer science05 social sciencesReal-time computingGeneral Engineering02 engineering and technologyObserver (special relativity)Traffic flowK4011-4343Transportation and communicationComputer Science Applicationstraffic flowCarriagewayHomogeneous0502 economics and businessSmartphone appDigital image processingTraffic conditionsdigital image processing technique0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingMean flowmoving observer methodautonomous vehiclesTransport and Telecommunication Journal
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Automated Railway Signs Detection. Preliminary Results

2019

Abstract Nowadays safety in railways is mostly achieved by automated system technologies such as ERTMS/ETCS. Nevertheless, on local railways (suburban and regional lines) several tasks still depend on the choices and actions of a human crew. With the aim to improve safety in such type of railways, this research proposes a system for the automatic detection and recognition of railway signs by means of the digital image processing technique. First field applications, carried out on the Italian railway network, show that the proposed system is very accurate (the percentage of correctly detected railway signs is about 97%), even at high train speeds.

railway safety050210 logistics & transportation0209 industrial biotechnologyComputer science05 social sciencesGeneral Engineering02 engineering and technologyK4011-4343Transportation and communicationrailway sign detection and recognitionComputer Science Applications020901 industrial engineering & automation0502 economics and businessdigital image processing techniqueRailway safety digital image processing technique railway sign detection and recognitionRailway safetyTransport and Telecommunication Journal
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