Integrating objective and subjective hazard risk in decision-aiding system design

Abstract

A generalized model is presented to incorporate objective (hard) and subjective (soft) hazard information in automated decision-aiding systems. The model may be used with more than one hazard, of more than one type, in a given problem. Uncertainties in state measurements, dynamics, hazard extent, and hazard severity are included, as is consideration of the fact that different operators may have different concepts of what is an acceptable or unacceptable risk. By examining the tradeoffs created by these uncertainties, appropriate decision thresholds can be selected. Using an aviation case study, information gained from observation of aircraft behavior in the presence of weather was used to develop a model of weather as a soft hazard. This information could then be used in a decision aid to provide feedback on route acceptability. IntroductionReal-time decision aiding and alerting systems are often used to assist human operators in controlling processes efficiently and in preventing undesirable incidents from occurring (such as a collision in a vehicle control application, or exceeding temperature limits in process control). There are a number of types of real-time decision aids, ranging from process status displays, to planning tools, to safety-and time-critical warning systems. However, all decision aids can be broadly classified as either active or passive. Active systems generate discrete decisions or commands that are communicated to the operator with the intent of modifying the process' future state trajectory (e.g., a traffic conflict resolution command). Passive systems provide process and environment state information to the operator (e.g., depicting precipitation levels on a weather radar display) without explicit decisions being made by the automation. Thus, an active system acts as an automated decision maker (which may agree or disagree with the human operator's decisions), while a passive system acts as an automated decision supporter.

Automatic flight control design considering objective and subjective risks during carrier landing

“…22 In particular, the previous data during landing are employed to further optimize the landing risk model. Kuchar et al 23 regard the risk as the operational constraint conditions in the decision-aiding system, which is an innovation idea. However, the studies above do not consider the future landing states and the subjective feeling of the pilots.…”

Analysis of Thunderstorm Effects on Aggregated Aircraft Trajectories

“…S EVERAL efforts in air traffic management research propose methods for constructing decision-support tools to increase the safety and efficiency of the air traffic control (ATC) system. As Kuchar et al [1] have noted, these tools will be accepted by the operators of the ATC system only if they are "aligned with operator mental models". Currently, understanding of how the risks created by thunderstorm activity are evaluated and how mitigation strategies are chosen by pilots and ATC system operators is limited.…”

“…Radar reflectivity and VIL measures were shown to be relevant to classification. Kuchar et al [1] studied a similar data set, taken during 12 h of operations at the Dallas-Fort-Worth TRACON and focusing on 353 penetration trajectories. The authors found that all aircraft avoided VIL level 6 activity, but that as VIL levels decreased, gradually more aircraft flew through storm activity for extended periods of time.…”