# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 3 Selecting Driving Locations for Multiple Paths  
In this paper, we consider a revolutionary transportation system, where a logically centralized remote controller controls all the vehicles in an area. A _driving location_ is a place where the remote controller or a driver controls a vehicle through communication networks. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Area** & **Main Research Issues** \\ \hline Autonomous Driving Concepts and Components & Autonomous driving needs many software components such as Localization, Mapping, Perception, Machine Learning, Dispatch \& Routing, Planning, Networking, and Controls [9; 10; 14]. \\ \hline Communication & Autonomous vehicles and remote driving vehicles need to exchange information between controlling center and the vehicle for monitoring and controlling purposes. Thus, the communication infrastructure is very crucial in the success of autonomous and remote driving [3; 16; 17; 19; 20; 27; 28]. \\ \hline Sensors & Remote driving vehicles have many sensors such as camera and LIDAR. The sensor information should be transmitted to the monitoring center and the delay should be within some delay bound. The relationship between the delay and the driving performance has been investigated a lot [17; 19; 21; 22; 23; 24]. \\ \hline Security & Since autonomous driving and remote driving may cause serious accidents due to many reasons, the security of those technology should be considered with the highest priority. Researches regarding security of autonomous and remote driving have been studied a lot [25]. \\ \hline Computation & Algorithms needed for autonomous and remote driving may require high computation time. Since the computation capacity of a vehicle may be limited, it is necessary to have additional computation power from elsewhere. How to distribute the computation job to various places can be a good research issue [26; 29; 30]. \\ \hline Node Placement Algorithms & Identifying driving location problem is similar to clustering problem given a number of points in a 2 or 3 dimensional area. The objectives can be reducing the average distance from the cluster centers or increasing signal strength in a wireless networks [31; 32; 33; 34]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing technologies. To start the discussion, let us consider a simple case with one vehicle. The vehicle has a route from an origin to a destination. A route in the real world is a sequence of road segments. For a remote driver to control the vehicle, the driver needs to communication with the vehicle through the communication network infrastructure. Since the vehicle is mobile, the edge of the communication network should be a base station, which has a direct _wireless_ link to the vehicle. So there must be a sequence of such base stations from the origin to the destination along the route. Since we focus on the remote driving technology, we only need to consider the sequence of base stations instead of the real route of roads itself. Thus, we consider a sequence of base stations as a route in this remote driving system. Actually, we assume that if the origin and the destination of a route are given, the sequence of base stations is easily obtained. In other words, the route of a vehicle can be easily represented by the sequence of base stations along the path. Thus, we assume that a vehicle to infrastructure communication, V2I, is established between a remote driver and the vehicle through base stations along the paths. As the vehicle moves toward the destination, the communication to the vehicle is transferred to the next base station along the path. Figure 3 shows two routes, each of which consists of a sequence of base stations. For example, the vehicle of \"Path 1\" may start the communication with \(B_{7}\), then it is reconnected to \(B_{8}\), and so on.