Reconstruction of missing retroreflexion data according to yearly inspection of markings

The quality and reliability of road infrastructure and its equipment play a major role in road safety. This is especially true if we are interested by autonomous cars traffic able to read road markings. This kind of vehicles needs an accurate maintenance strategy to guarantee a road with marking perceptible for a human eye or an autonomous car. To simplify the study of a road, a solution based on an Agglomerative Hierarchical Clustering segments a road according to the retroreflection level in time. If the follow-up of the maintenance for markings doesn't exist then a maintenance detector could estimate laying date. However, this strategy needs regular inspection data. Currently, French roads are irregularly inspected once a year and missing data appear. Three options are confronted: accept missing data, estimate missing data thanks to a linear interpolation or an original approach based also an Agglomerative Hierarchical Clustering. The last possibility evaluates the most reliable estimations for missing data. This approach is a first step to analyze the useful life of markings with a Weibull analysis. The broken centerline of the French National Road 4 is considered to illustrate our approach.


  • English

Media Info

  • Media Type: Digital/other
  • Pagination: 8p

Subject/Index Terms

Filing Info

  • Accession Number: 01688796
  • Record Type: Publication
  • Source Agency: Institut Francais des Sciences et Technologies des Transports, de l'Amenagement et des Reseaux (IFSTTAR)
  • Files: ITRD
  • Created Date: Dec 18 2018 10:14AM