# Event-Based Emergency Detection for Safe Drone
## 5 Conclusions  
Drone applications are widely studied in various fields. We captured the potential for urban supply-delivery applications. Unfortunately, many raise concerns about the reliability and safety of urban drone operations. Numerous studies on low-level drone-control algorithms have extended the capability and stability. Furthermore, recent studies have introduced fault-tolerant control methods and robust fault-detecting methods. However, we saw the demand to further extend the safety level to handle unexpected, abnormal situations that might occur in urban areas. The goal of our method was to implement a high-level event-based controller in drone emergency-detection matters. We proposed a new intelligent control methodology focusing on implementing partial situation awareness. This was achieved by introducing an approach based on the time-window scope. We designed our method to operate in autonomous drone waypoint missions, cooperate with existing low-level control methods, and ensure double safety. Figure 9: Comparison of average abnormal running time. Each case was conducted 100 times to reasonably compare the results. It turned out that too-early cases took more time to detect than too-late cases, where both too-late cases were detected within about 5.5 s on average. We designed a drone simulator and studied several emergency situations a drone might encounter in the real world. To calculate the expected interval time between way-points, we took a statistical approach and applied conditional variables to give a slightly lenient time-window range. We were able to confirm that our approach was effective and detected emergencies well, as shown by several case-study results. As an example, we simulated an extreme wind situation (i.e., wind speed exceeds permissible limit), and the drone diagnosed an emergency when typical PID controllers could not. Without the aid of our method, the drone might have resulted in a crash. In overall terms, our event-based approach was able to detect emergency (abnormal) events such as extreme winds and air collisions. It is true that in some cases, our approach resulted in slow reaction times. However, by applying advanced calibrations for calculating more accurate expected interval time in order to fine-tune the time-window with the placement of more narrow waypoint intervals, there seems to be more room for improvement. We found that by applying our control method using the time-window concept in conjunction with low-level control methods, we could better detect abnormal situations than low-level-only approaches. This is because our proposal implements an understanding of situation awareness, which is lacking in other sensor-based control methods. However, there remains a question about the robustness of our approach, since our method was only tested in simulation environments. We are currently working on real-world field tests to validate the robustness of our method. We also hope to compare emergency-detection and -handling performances with conventional drones sold on the market. In order to enhance the proposing method, we look forward to further extending our research in two major contexts. First, we plan to explore additional methods (e.g., deep learning [31]) to enhance the reaction time and robustness of event-based emergency detections, which is claimed to be a major weakness of our current proposal. Secondly, we wish to implement secondary position-estimating methods that operate without GPS data, since GPS errors are the biggest barriers to robust drone control in urban environments. Moreover, we want to remark that it is necessary for us to continue to consider other ideas that can enhance and upgrade our method. As an example, we believe that an adaptive control approach can aid in this context (e.g., [32]), which is a control strategy for stability and safety introduced initially for high-performance shipping systems.