Does the Condition of the Road Markings Have a Direct Impact on the Performance of Machine Vision during the Day on Dry Roads?

Author:

El Krine Abdessamad1ORCID,Redondin Maxime2ORCID,Girard Joffrey3,Heinkele Christophe1,Stresser Aude1,Muzet Valérie1ORCID

Affiliation:

1. Cerema ENDSUM Research Team (Evaluation Non Destructive des StrUctures et des Matériaux), 11 Rue Jean Mentelin, 67035 Strasbourg, France

2. Institut VEDECOM, 23 bis Allées des Marronniers, 78000 Versailles, France

3. Cerema EL Research Team (Eclairage et Lumière), 23 Avenue Amiral Chauvin, 49130 Les Ponts-de-Cé, France

Abstract

The forthcoming arrival of automated vehicles (AV) on the roads requires the re-evaluation or even adaptation of existing infrastructures as they are currently designed on the basis of human perception. Indeed, advanced driver-assistance systems (ADAS) do not necessarily have the same needs as drivers to detect road markings. One of the main challenges related to AV is the optimisation of the vehicle–infrastructure pair in order to guarantee the safety of all users. In this context, we compared the performance of a vehicle equipped with an ADAS machine-vision system with a dynamic retroreflectometer during the daytime on a road section. Our results questioned the reliability of the literature thresholds of the luminance contrast ratio on a dry road under sunny conditions. Despite the presence of old and worn road markings, the ADAS camera was able to detect the edge lines in more than 90% of the cases. The non-detections were not related to the poor condition of the markings but to the environmental conditions or the complexity of the infrastructure.

Funder

ADEME French project SAM

Publisher

MDPI AG

Subject

General Medicine

Reference35 articles.

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4. Carlson, P.J., Avelar, R., Park, E.S., and Kang, D. (2015). Nighttime Safety and Pavement Marking Retroreflectivity on Two-Lane Highways: Revisited with North Carolina Data, TEXAS A&M Transportation Institute. Technical Report 15-5753.

5. Pavement marking retroreflectivity and crash frequency: Segmentation, line type, and imputation effects;Bektas;J. Transp. Eng.,2016

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