Impact of Road Marking Retroreflectivity on Machine Vision in Dry Conditions: On-Road Test

Babić, Dario; Fiolić, Mario; Eichberger, Arno; Magosi, Zoltán Ferenc

doi:10.3390/s22041303

Cited by 6 publications

(3 citation statements)

References 19 publications

(22 reference statements)

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“…Reference [26] presented an overview of the interaction between road infrastructures and automated driving systems (ADS) and found out that international harmonisation of traffic signs and road markings is one of the challenges that must be faced in order to cope with future mobility. The results of the reference [27] showed that, with the increase of markings' retroreflection, the detection quality and the range of view of Mobil-eye increase. Reference [28] focused on novel smart road geometric design and review criteria to evaluate the impact of the novel system on road capacity and safety, and the case study results indicated that the expected potential percentage improvement in the lane capacity may reach values up to 205% with respect to the current lane capacity.…”

Section: Discussionmentioning

confidence: 98%

Study on the Safety Management of Connected and Autonomous Vehicle Test Roads Based on the Evaluation of Traffic Safety Facilities

Zhao,

Wen,

Pan

et al. 2023

PROMTT

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More and more connected and autonomous vehicle (CAV) open test roads reconstructed on the basis of traditional roads have appeared in China. However, the management policies vary, which makes the traffic environment complicated. This paper takes CAV test road safety management as the research aim and investigates the open test condition through the evaluation of the traffic safety facilities. Indicators were rigorously screened, then the game theory model was used to determine the combination weight of the indicators, and the set pair analysis was applied to solve the uncertain problems. A case study for the CAV test road network of a city in central China was implemented and the results show, regarding the traffic safety facilities’ condition, among the 20 sections of the city’s CAV test road network, 15% of which are at an excellent level, 75% of which are at a good level and 10% of which are at a moderate level; road signs, guardrail facilities, isolation facilities and road features are the main limiting factors affecting the level of traffic safety facilities. Based on the results, recommendations have been made for the transport management authorities in the aspects of safety management policy-making and facilities maintenance.

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Section: Discussionmentioning

confidence: 98%

Study on the Safety Management of Connected and Autonomous Vehicle Test Roads Based on the Evaluation of Traffic Safety Facilities

Zhao,

Wen,

Pan

et al. 2023

PROMTT

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“…In recent years, traffic patrols have become an important way to prevent and respond to emergencies in urban traffic management [ 21 ]. Through traffic patrols, it is possible to intervene in congestion and monitor vehicle violations, as well as road facilities and equipment.…”

Section: Related Workmentioning

confidence: 99%

A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search

Zhao

Cheng

et al. 2022

Sensors

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Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle–drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle–drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of “Drone-First, Vehicle-Then”. Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.

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“…They found that on-board vehicle sensors provide a robust system for agencies to collect quantitative data in relation to pavement marking visibility, and sunlight, weather, and vehicle speed can affect the detection results of vehicle lane keep assist systems. Babić et al ( 11 ) studied the measurement method of road traffic markings using Mobileye installed inside the vehicle cab, evaluating the mobile retroreflectometer’s measurements, and qualitatively analyzed the relationship between retroreflection level and detection quality of road traffic markings. Chou et al ( 12 ) used a dual-lens camera to collect road traffic marking images at night.…”

mentioning

confidence: 99%

LiDAR Perception and Evaluation Method for Road Traffic Marking Retroreflection

Huang

Han

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To solve the problem of low efficiency in retroreflection maintenance of road traffic markings, in this study a vehicle-mounted LiDAR-based perception and evaluation method of retroreflection of markings is proposed. First, this method establishes a calibration prediction model for LiDAR based on a regression decision tree. Then, a marking maintenance evaluation model is constructed in combination with the decision threshold proposed by China’s national standard, and the accuracy of the maintenance evaluation model is analyzed using F1-score, recall, and precision. In this study, four marking lines were used as the calibration data source, and a dataset of an independent 1,300 m road section was used to verify the established models. The results show that the coefficient of the retroreflected luminance ( RL) and the reflection intensity of the markings are positively correlated. During the construction of the calibration prediction model, the multiple linear regression functions, the second-order polynomial functions, and the decision tree are compared, and the result indicates that decision tree has the best fit to the data with the coefficient of determination for the established calibration prediction model better than 0.95. The agreement between the maintenance decision obtained from the maintenance evaluation model and the traditional method is more than 85%. The time cost is reduced by at least 90%. The proposed calibration prediction model can accurately predict the RL, and can quickly collect the RL values of the in-service road traffic markings. The proposed maintenance evaluation model is highly efficient and can replace the traditional evaluation method for markings.

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Impact of Road Marking Retroreflectivity on Machine Vision in Dry Conditions: On-Road Test

Cited by 6 publications

References 19 publications

Study on the Safety Management of Connected and Autonomous Vehicle Test Roads Based on the Evaluation of Traffic Safety Facilities

Study on the Safety Management of Connected and Autonomous Vehicle Test Roads Based on the Evaluation of Traffic Safety Facilities

A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search

LiDAR Perception and Evaluation Method for Road Traffic Marking Retroreflection

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