## 1 Introduction
### Literature Review  
In the direction of autonomous driving, the connection between vehicles has been proven to be of significant importance, allowing faster improvements and refinements, such as road mapping, traffic management, and reducing the number of accidents due to human errors. Localization and mapping of road networks are continuously evolving, starting with indoor mapping, which is usually applicable to mobile robot scenarios. The current progress brings us to advanced, dynamic mapping of roads, which is performed during standard vehicle utilization. In this case, two methods are applied to obtain a more efficient way of locating the vehicle in a certain environment and map specific characteristics of that environment in order to construct a complex model through which an AV is navigating. The first method implies using a detailed, already created map and do all the necessary computations for localization during autonomous driving. The second approach is more similar to humans' way of driving a vehicle by selectively mapping spots or areas, as described by [PERSON] [17]. This is shown in Figure 1.  
Applications of AVs are increasingly common in the passenger transport area, as autonomous buses, shuttles, or even taxis can be observed inside airports or other controlled environments, but also on the public roads, where public transport is revolutionized by self-driving solutions that ensure a safer, non-stop alternative to personal transport. Surprisingly, investments and interest in such advanced technologies are not shown only by private companies, which use autonomous options to carry their employees inside technology or industrial parks or to and from work. Various state-owned companies and municipalities have been pioneering these solutions to some extent by implementing special routes of transportation in controlled environments, such as closed communities, and even on open roads, connecting main access hubs into the cities (e.g., airports, train stations, bus terminals) with points of interest (e.g., city centers, public venues). Some of the most common applications aim to connect the venues or access points that operate around the clock and provide constant transit for people. Notable applications are found worldwide, showing various important manufacturers linking with private or public entities in multiple projects, such as:  
* MAN and Mobileye (an Intel-owned company)--development of autonomous buses in the context of the MINGA research project in Munich [18];  
Figure 1: Overview of the autonomous navigation process [17]. ZF--investments and partnerships aimed at pushing autonomous shuttles into the market [19];
* Navya and Yutong--access to subsidies used mainly for projects linked to the government's \"France Relance\" program, as well as receiving a Red Dot Award in 2021 [20];
* Volvo and Keolis--bringing the concept of a 7900 electric city bus to reality after publicly presenting the first self-driving bus in 2019, along with Nanyang Technological University (NTU) [21];
* Scania and Nobina--first implementation of autonomous buses on the streets of Stockholm, starting with two units, each having a driver onboard, responsible for monitoring the driving activity [22];
* Scania and Haylion Technologies--jointly working on a sustainable transport system solution in the Chinese public transport area [23];
* ADL and Stagecoach--implementation of autonomous diesel buses on Scotland's roads, financed by the UK government [24];
* Iveco and EasyMile--development of autonomous systems that can be added to buses so the vehicles can maneuver themselves inside depots, performing parking tasks to maximize available space, refuel, wash, and enter or exit the depot without human involvement [25]. Most of the above-mentioned applications, as well as other similar ones, are intended to work as a fleet of vehicles, communicating with one another and ensuring the completion of tasks such as running a scheduled people transport service or even on-demand transfers.