An Efficient Driver Selection Algorithm for Controlling Multiple Vehicles in Remote Driving

Recently, some researchers consider remote driving as an augmentation of autonomous driving as level four autonomy in which the driver can rest while driving is not likely to be achieved in a near future. Besides functioning as an augmentation to autonomous driving, we consider remote driving as a major component to transform the current transportation system in a more fundamental way. Basically, all the vehicles in an area can be controlled by some remote controllers or drivers so that transportation can be performed in a more efficient way. For example, if a remote controller decides all the routes of the vehicles, the road capacity can be efficiently used so that the vehicles can arrive at the destinations faster. Fuel efficiency can also be achieved by creating a platoon with the vehicles having similar routes. However, one of the biggest challenges in remote driving is the communication latency between the remote driver and the vehicle. Thus, the remote drivers should be within a certain latency limit to avoid any type of safety problem. Actually, in our past work, we have introduced an algorithm called Longest Advance First (LAF) to select the locations of remote drivers for a single vehicle in a single journey. LAF can achieve an optimal selection of drivers in terms of the number of drivers. In this paper, we consider a remote driving framework where multiple vehicles are controlled by remote drivers at the same time. Applying LAF to each route may not be optimal for multiple routes. Thus, we propose a heuristic algorithm that can select locations of remote driving facilities in which remote drivers drive vehicles for multiple routes. Through simulations, we show that the proposed algorithm shows much better performance than applying LAF multiple times.