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.

1. (2018). Road Marking Materials—Road Marking Performance for Road Users and Test Methods (Standard No. EN 1436). European Standard.

2. (2005). Standard Test Method for Measurement of Retroreflective Pavement Marking Materials with CEN-Prescribed Geometry Using a Portable Retroreflectometer (Standard No. E1710-05).

3. Investigation of longitudinal pavement marking retroreflectivity and safety;Carlson;Transp. Res. Rec.,2013

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

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining;Journal of Advanced Computational Intelligence and Intelligent Informatics;2024-05-20

2. Hybrid AI Road Markings Analysis from a Retroreflectometer;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3