This paper proposes an amendment of the classification of safety events based on their controllability and contemplates the potential of an event to escalate into higher severity classes. It considers (1) whether the end-user had the opportunity to intervene into the course of an event, (2) the level of end-user familiarity with the situation, and (3) the positive or negative effects of end-user intervention against expected outcomes. To examine its potential, we applied the refined classification to 296 aviation safety investigation reports. The results suggested that pilots controlled only three-quarters of the occurrences, more than three-thirds of the controlled cases regarded fairly unfamiliar situations, and the flight crews succeeded to mitigate the possible negative consequences of events in about 71% of the cases. Further statistical tests showed that the controllability-related characteristics of events had not significantly changed over time, and they varied across regions, aircraft, operational and event characteristics, as well as when fatigue had contributed to the occurrences. Overall, the findings demonstrated the value of using the controllability classification before considering the actual outcomes of events as means to support the identification of system resilience and successes. The classification can also be embedded in voluntary reporting systems to allow end-users to express the degree of each of the controllability characteristics so that management can monitor them over time and perform internal and external benchmarking. The mandatory reports concerned, the classification could function as a decision-making parameter for prioritising incident investigations.
Most safety oriented organizations have established their accidents classification taking into account the magnitude of the combined adverse outcomes on humans, assets and the environment without considering the accidents‟ potential and the actual attempts of the involved persons to intervene with the accident progress. The specific research exploited a large sample of an aviation organization accident records for an11 years‟ time period and employed frequency and chi-square analyses to test a new accident classification scheme based on the distinction among the safety events with or without human intervention on the accident scene, indicating the management or not of their ultimate consequences. Furthermore, the research depicted the effectiveness of personnel strains to alleviate the accident potential outcomes and studied the contribution of time, local and complexity factors on the accident control attempt and the humans‟ positive or negative interference. The specific newly proposed accident classification successfully addressed the “controlled” or “uncontrolled” traits of the safety events studies, prior their severities consideration, and unveiled the effectiveness of personnel efforts to compensate for the adverse accident march. The portion between controlled and uncontrolled accidents in terms of the human intervention along with the effectiveness of the later may comprise a useful safety performance indicator that can be adopted by any industry sector and may be recommended through international and state safety related authorities.
This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.
