# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 9 Conclusions  
We proposed a 3D intersection model for UAV traffic in the sky above an urban area. Our intersection model consists of a 3D intersection and approaching areas. We were able to define the behavior of the UAVs in the approaching areas and make the UAVs cross the  
Figure 31: Maximum number of UAVs in the intersection when \(s_{max}=19\) m/s for different values of \(s_{min}\).  
Figure 32: Snapshot of the intersection when there are 72 UAVs in the intersection in (**a**) 3D view and in (**b**) x-y view. One UAV is represented by one color and UAVs coming from the same way have the same color.  
intersection as quickly as possible. For this work, we assumed that the UAVs behave as instructed by the intersection manager and communication is assumed to be highly reliable.  
To take into account the possible disturbances to the speed, we introduced a range of speed for the UAVs. We think that the speed range, to a certain extent, can reflect the effect of the variance of the actual speed. The speed range can easily be changed in our model and the effect of different speed ranges to the system's efficiency are shown in the simulation results. We think that the UAVs may move at the allowed speed range since current UAVs have the capability to follow the instructions very accurately.  
For the model considered in this paper, we were able to show that our system works as expected. Through simulations, we were able to show that UAVs can cross the intersection efficiently. The efficiency is compared with the static optimal traffic light scheme to show that our system is highly efficient. However, when the intersection type or the number of layers changes, additional works are required. For example, we need to make changes on the allowed movements of the UAVs in the intersection.  
We assumed in this work that there are no communication errors and delay is negligible. If the communication delay is significant or some messages (request or response) are lost, the associated UAVs may need to stop, which in effect will degrade the performance of the system. The effect of communication errors or messages lost on the system performance remains as a future work.  
Another limitation of our work is that we assumed that UAVs could obtain their positions accurately. The position information of the UAV included in its reservation request needs to be accurate. Otherwise, the order of UAVs' arrivals may be incorrectly determined. To address this problem, we may need to add information in the request that specifies which UAV is ahead of which. We also need the accurate position of the UAV to correctly compute the time it enters the intersection. To consider the position inaccuracy in this issue, we may need to allow for certain errors in the position of the UAVs in scheduling them. Aside from the previously mentioned problems, if the UAV cannot obtain and provide accurate position information, it cannot behave in the approaching area as expected. This problem can be solved by increasing the lengths of the reservation zone and queueing zone appropriately. The effect of the inaccuracy in the position information to the performance of the system also remains as a future work.  
Conceptualization, M.C. and H.-H.C.; Formal analysis, A.R. and M.C.; Funding acquisition, M.C. and H.-H.C.; Methodology, A.R. and M.C.; Project administration, M.C. and H.-H.C.; Software, A.R. and H.-H.C.; Supervision, M.C.; Validation, A.R., M.C. and H.-H.C.; Writing--original draft, A.R.; Writing--review and editing, M.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049577 and NRF- 2017R1D1A1B03035324). This fund will also cover the costs to publish in open access. The authors declare no conflict of interest.