Visibility of various road markings for machine vision

Burghardt, Tomasz E.; Popp, Roman; Helmreich, Bernhard; Reiter, Thomas; Böhm, Gerald; Pitterle, Georg; Artmann, Matthäus

doi:10.1016/j.cscm.2021.e00579

Cited by 24 publications

(17 citation statements)

References 33 publications

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“…We argue that LiDAR intensity is a useful feature to characterize the paint of road markings based on the work of Burghardt et al. (2021) on machine vision and LiDAR data concerning the visual recognition of road markings. In this work, different road markings made of different materials with different colors and retroreflectivities were studied under different lighting, rain, and fog conditions.…”

Section: Resultsmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routes.

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Section: Resultsmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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“…They suggest a shift from traditional road maintenance methods towards adaptive strategies that meet the intricate requirements of autonomous navigation systems. Clearly, updating the standards for road marking retro-reflectivity is not only a matter of regulatory compliance, but also a critical step towards optimizing the financial and functional aspects of road infrastructure management in the era of autonomous driving [ 28 , 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles

Jeong,

Ko,

Lee

et al. 2024

Sensors

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The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.

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“…Road segments where either no high-definition map is available or where a mixed traffic of conventional and CAV traffic is expected, CAVs need to detect and recognize road signage using their own sensors. Numerous studies, e.g., [ 29 , 30 ], conclude that the features of road markings that are key for their recognition by human drivers, such as retroreflectivity and contrast, are also important in the case of marking detection by CAVs.…”

Section: Framework For Assessment Of Physical and Digital Infrastruct...mentioning

confidence: 99%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision.

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Visibility of various road markings for machine vision

Cited by 24 publications

References 33 publications

Characterizing zebra crossing zones using LiDAR data

Characterizing zebra crossing zones using LiDAR data

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

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