**1. Introduction**

Lane departure crashes are one of the most common types of road accidents. In the US alone, 51% of all fatal accidents are caused by lane departure, i.e., a vehicle crossing the edge or the centre line [1]. There is a variety of contributing factors involving the driver, the vehicle, the road and its surroundings (the environment) but it is proven that rural roads with low traffic volume and density are more likely to contribute to road departure accidents [2]. This is mainly due to higher travelling speed, distracted driving and/or fatigue. Different safety measures have shown positive results in decreasing lane departure accidents [3–5], yet the overall problem still exists.

A potentially promising solution to the aforementioned problem lies in automated driving and Advanced Driver Assistance Systems (ADAS), which perceive the static and the dynamic content of the environment around the vehicle and thus assist the human driver in driving. An important task during environment perception is lane detection needed for Lateral Support Systems (LSS), which comprise of lane departure warning and/or lane keeping assistance. The main purpose of LSS is to prevent road accidents caused by road departure or entrance in the lane of other vehicles. Due to high fatality

**Citation:** Babi´c, D.; Babi´c, D.; Fioli´c, M.; Eichberger, A.; Magosi, Z.F. A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision. *Energies* **2021**, *14*, 4666. https:// doi.org/10.3390/en14154666

Academic Editors: Guzek Marek, Rafał Jurecki, Wojciech Wach and Thanikanti Sudhakar Babu

Received: 10 June 2021 Accepted: 30 July 2021 Published: 1 August 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

rate in such accidents, the use of different LSS could significantly improve the overall road safety [6–9].

In literature, two main technologies for LSS are reported: LIDARs (Light Detection and Ranging) and vision-based cameras. The main advantage of LIDAR is the fact that it uses an active light source and thus does not rely on the variabilities associated with external/natural lighting as do regular vision-based cameras [10]. However, LIDAR technology is usually used for adaptive cruise control, while lane detection is only partially feasible and mainly used in expensive demonstrator vehicles for highly automated driving [11]. Therefore, current market-ready systems use passive vision-based cameras and image processing to collect and analyse the data from roads [12]. In general, camera-based lane detection starts from image pre-processing which includes different corrections of the collected image (such as exposure correction and shadow removal) and feature extraction. This is then followed by feature detection and model fitting, and then time integration to keep temporal and position consistency [13].

The proper function of LSS depends on several factors [11,14,15]: the quality of the camera (focal distance and camera velocity), condition, colour, width and visibility of lane markings (daytime visibility, night-time visibility—retroreflection and contrast to pavement), lane marking configuration (full/dashed, length of dashed lines), driving speed, weather conditions, general visibility of the environment, sun direction, pavement characteristics (type, condition and texture), geometry of the road, type of road edge (structured/unstructured) and combinations of the above factors.

Several studies have investigated how lane markings' characteristics affect the accuracy of lane detection under different conditions. One of the first such studies was conducted in Sweden in 2010 with the aim of testing various types of lane markings (flat/profiled, new/existing) under different weather and lighting conditions [16]. The study concluded that in dry daytime conditions, the luminance coefficient must be at least 5 mcd/lx/m<sup>2</sup> higher than the road surface and that it should be at least 85 mcd/lx/m2. Furthermore, the study also found that roads wider than seven meters need to have a centre line in order for LSS to become active. Finally, the study highlighted the importance of increased visibility of lane markings in wet and rainy conditions. In 2016, a research was conducted with the aim of identifying the effects of lane markings' characteristics (width, colour and retroreflectivity) on the performance of a machine-vision system [17]. The study concluded that the view range of the investigated machine-vision (Mobileye) is between 6–18 m in front of the vehicle and that, at night-time, the retroreflectivity of lane markings affected the reading quality. Namely, lane markings with higher retroreflectivity increased the reading level and confidence. Also, wider lane markings (15 cm width) were read better when compared with narrower markings (10 cm width), regardless of stripe colour. Similar results were obtained in a 2017 study [18]. The results indicated that the machine-vision (Mobileye) detection of lane markings generally increased with the increase of retroreflection and contrast ratio. However, the authors highlighted that factors such as light bloom from a low-angled sunlight or visual occlusion from rain, snow, or fog may also influence the detection and readability of machine-vision. Furthermore, such systems generally detect markings with the minimal retroreflectivity of 100 mcd/lx/m<sup>2</sup> but do not necessarily provide the strongest detection. An extensive study, which consisted of interviews of stakeholders and on-road and off-road testing, was conducted in Australia in order to determine the implications of road markings for machine vision [11]. Testing scenarios included several test cases which included the impact of different road markings' characteristics (daytime dry luminance coefficient—Qd, daytime dry contrast ratio, day wet contrast ratio, night dry retroreflectivity—RL, night dry contrast ratio, night wet (recovery) retroreflectivity—RL, night wet contrast ratio width, marking width), different complex situations (such as road markings' perceptual measures), non-marked edge line, road curvation etc. The authors used several vehicles and a Mobileye camera to test lane detection depending on different scenarios. Based on data analysis, it was concluded that machine-vision detection of solid lines is "better" when compared to dashed lines with

same characteristics (equal width, brightness and maintenance). Weather conditions also impacted machine-vision readings differently. Namely, minimal ambient lighting (such as streetlights or low-angled sunlight) may improve the contrast ratio due to reduced specular diffusion and, thus improve line detection. On the other hand, with excessive ambient lighting, machine-vision systems can suffer from 'light bloom'. Furthermore, the contrast ratio for night-time visibility of between 5-to-1 and 10-to-1 between lane markings and the surrounding substrate is needed for proper functioning of the machine-vision system. Also, lane detection during the day was generally less effective than at night-time due to the complexity of visual clutter evident during daylight hours and the fact that retroreflective properties of well-maintained lane markings provide greater contrast during night-time. In addition, the study found that several other factors, such as driving speed, marking width, maintenance practices etc. influence the accuracy of machine-vision.

Most recently, a researcher at the Department of Civil Engineering and Architecture at the University of Catania used Automatic Road Analyzer (ARAN) and Mobileye 6.0 system to investigate how different road factors (road characteristics and conditions) impact the performance of the LSS system [19]. The ARAN was used to obtain measures of road geometric characteristics (cross section, gradients, horizontal and vertical alignment) which were then synchronised with the Mobileye. In addition, the luminance coefficient of the lane marking in diffuse lighting conditions (Qd) was detected with a portable retroreflectometer. Based on the data analysis using a Decision Tree Method, authors concluded that when daytime visibility (Qd) of road markings is lower than 153 mcd/lx/m2, the probability of LSS failing rises to 11.4% for the calibration sample and 14.35% for the validation sample. Also, curved road sections (with R < 141 m) showed a higher percentage of faults than the average 3% in the test conditions. On the other hand, the average driving speed did not result in any significant changes in LSS accuracy. Overall, the results suggest that a Qd higher than 153 mcd/lx/m2 improves the detection of lane markings using a Mobileye lane detection system.

Based on literature findings, one can conclude that the function of LSS is influenced by a number of factors and their interaction. However, it is still largely unknown to what extent each factor influences LSS, precisely due to their high number and their interaction (one factor may significantly influence another one in certain conditions). The aforementioned can be seen from the example of "visibility factor", i.e., lane detection between daytime and night-time. Several studies indicated that the accuracy of lane detection varies between day and night-time, however it is still largely unknown to what extent these differences go. For this reason, the aim of this study is to conduct on-road tests in order to determine and compare the detection quality and view range of the machine-vision system during dry daytime and night-time conditions. Based on the aforementioned aim and literature review, the hypotheses of the study are as follows:


The results of the study are important for two reasons. First, they provide a valuable input to researchers and developers regarding the "real-world" functioning of machinevision lane detection. The insight into the variations of lane detection accuracy in different visibility conditions may help further development of such systems. Second, the results may help road authorities in optimizing the quality performance of road markings. Namely, it is still not entirely clear what the minimal quality requirements for road markings are in order to provide adequate accuracy of machine-vision. However, knowing which visibility conditions are more problematic for machine-vision may help road authorities in prioritizing maintenance activities as well as defining minimal visibility levels of road markings.

The manuscript is structured in five main sections. Section 1 presents the research problem and literature findings related to the impact of road markings quality on LSS. Section 2 describes the research equipment, testing procedure, road sections chosen for the purpose of this research and data analysis. The results of on-road tests are divided into two subsections presented in Section 3: Quality of lane markings' detection and view range of lane markings. In Section 4 the obtained results are discussed and compared to findings from previous studies, including limitations and suggestions for future studies. The last section (Section 5) presents the conclusion of the study and provides potential practical application for the obtained results.

### **2. Materials and Methods**

#### *2.1. Apparatus*

The data related to lane detection was recorded using Mobileye 630 system implemented in the testing vehicle (BMW640i) of the Institute of Automotive Engineering, Graz University of Technology (Figure 1). The system is developed by a leading supplier of camera systems for ADAS and it was previously used in several studies [11,14,17,19]. The Mobileye 630 system uses a digital camera with the 38 degrees horizontal and 28 degrees vertical field of view located behind the front windshield inside the vehicle. Using image processing chips, the camera enables high-performance real-time image processing (15 frames per second) of different objects on roads such as lane markings, pedestrians etc. The system, among others, conforms to the Directive 72/245/EEC for electronic equipment which can be built in road vehicles and enables extraction of recorded data for further processing. For the purpose of this study, we recorded the data related to the type of detected longitudinal marking (continuous or dashed), approximate marking width, view range and the quality of the marking both for middle and edge lines. The vehicle was also equipped with a precise measurement system to record the vehicle's trajectory by a combination of GPS localization (Novatel OEM-6–RT2 receiver) and inertial measurement unit (GENESYS ADMA G-III).

**Figure 1.** Testing vehicle with Mobileye 360 implemented behind the front windshield inside the vehicle.

#### *2.2. Test Road Sections and Procedure*

The study was conducted on four rural road sections in Croatia in total length of 120.8 km. The roads were two-way roads with 3.5 m lane width and low traffic volumes. Three road sections were marked with the middle line and partially with edge lines while the fourth road had only the middle line. All markings were white, 15 cm wide and made from solventborne paint (Type I). The main characteristics of the road sections are presented in Table 1.


**Table 1.** Characteristics of test road sections.

The roads were selected based on the fact that they are rural with low traffic volumes. Rural roads with low traffic volumes have higher risk of road departure accidents [2] and thus the importance of LSS is increased. Also, roads were selected since they have the recommended width of lane markings for LSS [20–22] and on the majority of their length, there is no road lighting. Road lighting was present only on short sections located in populated areas. However, these sections were excluded from the analysis (see Section 2.3).

Each road was "measured" twice: once during daytime and once during night-time. The measurements for night-time conditions were conducted on 21 September 2020 and on 22 September 2020 for daytime conditions. The measurements were done between 10:15 h and 13:00 h during daytime and between 19:20 h and 22:20 h during night-time. In both conditions, the weather was dry and the sky clear. The driving speed was in accordance with the speed limit and differed between 60 km/h and 80 km/h. In this way, we tried to control the impact of speed on the detection of markings since literature suggests that speed has a varying impact on machine-vision (some improve at higher speeds, some degrade) [11].
