## 4 AVs Critical Issues: Aspects Related to Specific Infrastructures Components
### Autonomous Vehicles Detection Capability at Intersection and for Horizontal Road Markings
This system increases the admissible road traffic volume and relies on a large and increasing amount of data to be exchanged between AVs and IM. Hence, its efficiency depends on the accuracy of this shared data. Robust Intersection Manager's system adds the benefit of tracking vehicles' trajectories approaching an intersection associated with a Velocity of Arrival (VOA) and Trajectory of Arrival (TOA) as they get close to the junction. This situation leads to increased accuracy and higher safety levels. This system is still subject to hardware or communication errors and even to hacking attempts interfering with vehicle positioning and speed detection. Figure 4 below depicts the dynamics of an accident happening with these systems. Figure 4: Accident dynamics on IM systems [31]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Marking Type** & **Pavement Marking** \\ \hline Lane Open & \\ Lane Closed & \\ Right Turn Single Lane & \\ Ramp Closure & \\ Right Turn For Single Lane & \\ Right Ramp Closure & \\ Merge Right & \\ Left Turn Two-Lane & \\ Ramp-Left Lane Closure & \\ Left Turn Two-Lane & \\ Ramp-Right Lane Closure & \\ Left Turn Single Lane & \\ Ramp Closure & \\ U-Turn & \\ Start Work Zone & \\ End Work Zone & \\ Work Zone Edge Line & \\ (Contrast Pavement Marking) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: New markings for AVs movement in work areas [28]. The use of new strategies, based, for instance, on the Right-of-Way Assignment (RWA), can improve traffic dynamics in the presence of non-signalized intersections. This strategy is based on marking short-term decisions for the right of ways and on one-to-one communications. Other assumptions include that two vehicles can cross the intersection simultaneously and that pilot vehicles can obtain information about speed, location, and driving intentions of other surrounding vehicles. Moreover, priority levels and precedence rules are defined. A related strategy, called Cooperative Driving Strategy (CDS), introduces the assumption and possibility that the information connecting all the elements involved in traffic (pedestrians, bicycles, and vehicles, to name a few) is available via a V2X communication system, which can cover up an area of 300 m or even more. This methodology splits the junction in a grid made of several smaller areas, regulating vehicle traffic by setting up the minimum time interval between two vehicles. Results from dedicated research studies [32] show that this approach leads to higher performances compared, for example, to a traditional Responsibility Sensitive Safety (RSS) approach (which applies a typical First-In/First-Out (FIFO) strategy with few communications). Figures 5 and 6 show a significant difference in performances between RWA/CDS and RSS, specifically in terms of average delay/arrival date and throughput/arrival rate ratios. Simulation models for AVs at intersections can also be trained thanks to machine learning, specifically Reinforcement Learning (RL) and computer vision. Aerial photographs can provide visual information about the road intersection to create a simulated model. The vehicles' position, both autonomous and manned, can be detected using deep neural networks of various kinds [33]. Vehicles in the intersection can be detected with the segmentation technique, starting from a full-color aerial image of the crossroad captured with drones and then post-processed, moving through computerized Fully Convolutional Network (FCN), creating a B/W image which then becomes clustered through a DBSCAN. With the OpenCV CVAT software tool, vehicles on the aerial images are marked and labeled with polygons (rectangles and ellipses), indicating the relative location. Possible trajectories are marked with polylines as well. Vehicles are finally manually removed from the image, leaving the polygons on. The image becomes then binarized and clustered into closed regions with the DBSCAN algorithm, filtering and discarding the smaller regions.