## 4 AVs Critical Issues: Aspects Related to Specific Infrastructures Components
### Autonomous Vehicles Detection Capability at Intersection and for Horizontal Road Markings  
The quality of horizontal road markings plays a crucial role for autonomous vehicle detection, not only to avoid conflicts but also to facilitate pedestrian individuation, for instance, in zebra crossing points. Hence, ADAS (Advanced Driving Assistance Systems) equipped by AVs must ensure an efficient and smart detection of road markings, primarily due to their frequent deterioration (mainly caused by severe weather and actions from rolling wheels) and also to poor maintenance. Road markings are fundamental for vehicle lane-keeping, with straight or curved lines substantially characterizing these signal types; additionally, horizontal markings may also include directional arrows, helpful for the AVs' decision-making process. However, directional arrows can be perceived differently than simple lines by visual cameras due to several factors like angles of view, line thickness or interference of shadows projected by nearby objects. In AVs, the detection process of horizontal road markings occurs through vision-based systems that result to be the more appropriate, mainly for economic reasons thanks to the inexpensiveness of vision cameras. Moreover, these systems also allow the creation of 3D input data whose quality is optimal as one of LiDAR's systems [25], useful considering that visual data is the most suitable input for human drivers. Therefore, road marking detection has a dual approach, the first related to AV detection capabilities and the second to the quality of horizontal road markings. The latter represents an aspect of vehicle circulation affecting safety regardless of autonomous performances and MV (machine vision) capabilities. Since 1992, [PERSON] highlighted how to correct maintenance of road markings involves at least sixty times less than costs caused by incidents caused by a lousy perception of roadway spaces. The parameter that mainly affects MV's road markings perception is the retro-reflectivity (RL), especially in low light hours. Such feature represents the capability to reflect radiations and can be measured as \(\mathrm{mcd/m^{2}/lx}\). In 2016, [PERSON] et al. demonstrated that, when road markings had an RL = 50 \(\mathrm{mcd/m^{2}/lx}\), MV systems began to show malfunctions, while other studies, like the one of [PERSON] in 2017, reported that MV was also affected by width and color, where white was more suitable to be detected compared to yellow [2]. Several methods have been studied to facilitate road markings detection with various processes. For example, [PERSON] and [PERSON] provided a system characterized by two video-based driving tasks. At the same time, [PERSON] combined AVM camera features with \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenarios** & **Los** & **Vehs** & **Fuel Consumption** & **Co** & **Nox** & **Voc** \\ \hline No-Control & LOS F & 5509.00 & 3770 & 69,613 & 13,544 & 16,133 \\ ALINEA & LOS D & 5759.00 & 2319 & 42,822 & 8332 & 9925 \\ VSL & LOS D & 5692.00 & 2140 & 39,511 & 7687 & 9157 \\ VSL+ALINEA & LOS D & 5741.00 & 1902 & 35,114 & 6832 & 8138 \\ VSL+ALINEA/B & LOS C & 5904.20 & 415 & 29,024 & 5647 & 6726 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Improvement of LOS, fuel consumption, and emissions with the application of specific algorithms [23]. LiDAR's data. Ultimately, [PERSON] and [PERSON] proposed an algorithm able to speed up map rendering by solving point cloud data [26]. Some of these studies were also analyzed through a forecasting process with simulation software. [PERSON] and [PERSON] carried out their proposal of a robust road lane markings detection system with simulator CARLA that showed how the system resulted inefficient in straight parts. At the same time, it did not present beneficial results in curves [27].