Jayaprakash Suraj Nandiganahalli

User Interface Validation using Mode Confusion Detection

Due to the rapid advancements in the digital avionics systems, the aircraft operation is getting more automated. However, this does not completely exclude the operations carried out by the pilot via interaction with the automation through the User Interface (UI) (or more commonly known as ight deck instruments). At times, this interaction raises situational awareness issues to the pilot about the behavior of the automation. One such critical issue is mode confusion where a mismatch occurs between the behavior of the automation expected by the pilot and the actual behavior of the automation. This paper examines the mode confusion detection problem from the UI design perspective by taking a control-theoretic perspective on dynamical systems. The automation is modeled as a hybrid system and the pilot is modeled as an intent-based discrete-event system for the given UI which is modeled as a lter of ight information. By inferring and comparing the intents of automation and pilot using a host of information such as the aircraft’s continuous states and ight modes, and automation’s and pilot’s inputs, this paper attempts to validate the given UI via mode confusion detection. The causes responsible for the detected mode confusion are used to suggest improvements to the existing UI design, thereby improving the situational awareness of the pilot and aircraft safety. The framework is tested on two representative mode confusion incidents/accidents and the associated UIs are validated.

Formal Extensions to the Intent-Based Mode Confusion Detection Framework

Due to the increasing use of automation in the traditionally human-centered flight deck, emergent problems such as mode confusion are becoming increasingly important to be addressed. Mode confusion typically happens due to a mismatch between the actual aircraft state and the state the pilot expects. In earlier work, we proposed a novel intent-based mode confusion detection framework for the User Interface (UI) validation based on a control-theoretic perspective, where the automation’s behavior was modeled as a hybrid system and the pilot’s behavior was modeled as a discrete event system with intents as its states. By inferring and comparing the intents of the automation and the pilot utilizing the different available information such as the aircraft’s continuous state and discrete flight modes, and the automation’s and pilot’s control inputs, the mode confusion was detected and the UI was validated. However, two practical issues such as the complexities in the pilot’s behaviors such as forgetfulness, time-delayed inputs, etc. and the unavailability of the automation’s logic in general were not considered. The main objective of this paper is to analyze those issues and propose formal solutions to facilitate accurate intent inference of the automation and the pilot so that the pilot’s situational awareness and the aircraft safety can be enhanced. The augmented framework is validated on simulated flight data and results are discussed.

Formal Verification for Mode Confusion in the Flight Deck Using Intent-Based Abstraction

As the flight deck has become highly automated, mode confusion between the pilot and the automation has emerged as an important issue for aviation safety. This paper presents a formal verification framework that can be used to efficiently detect a wide range of mode-confusion problems in the pilot–automation system and provide safety guarantees. To facilitate this, a novel formal modeling of the automation and pilot is proposed to efficiently verify the pilot–automation system. The automation of the aircraft is modeled as a deterministic hybrid system, and the pilot is modeled as an intent-based finite state machine. Due to the high dimension of the aircraft’s continuous states and the large number of flight-mode combinations, formal verification of the hybrid system is computationally formidable, leading to the state-space explosion problem. To tackle this problem, a computationally efficient abstraction method for the hybrid model is proposed using intent inference, from which an intent-based finite sta...

Diagnostic Throughput Factor Analysis Tool for En-Route Airspace

Today’s National Airspace System (NAS) is facing a challenge of dealing with an increasingly larger number of flight operations. To address this, the Next Generation Air Transportation System (NextGen) is introduced by the Federal Aviation Administration (FAA) to improve the efficiency and safety of the NAS. Currently, the performance of the NAS is mainly measured using the delay-based metrics that cannot capture the positive aspects of the performance and level of utilization of the NAS. To address this issue, a diagnostic throughput factor analysis tool is proposed to analyze and quantify the factors that have greater responsibility for poor/better regions/times of performance of the en-route airspace using the concept of throughput. Through a function-level comparison with other applications such as ground transportation, manufacturing, and wireless communication, major factors affecting the NAS’s throughput performance are identified as Monitor Alert Parameter violation, aircraft conflict, metering, c...

Intent-based Abstraction for Formal Verification of Flight Deck Mode Confusion

ion of domain: To succinctly describe the aircraft’s motion for the purpose of mode confusion detection, the hybrid states X ×Q are mapped to the intent domain I. The flight intent of the automation I k ∈ I is defined based on the sign of the continuous state derivatives (i.e., ẋ , where is a small positive number to account for uncertainties) for each discrete flight mode qk. This is mathematically given by z : X ×Q→ I as: I k = z(xk, qk), I ∈ I (4) For example, if (ḣk > 0) ∧ (qk := V/S mode), then I k = climb, where ∧ is the logical and operator. Thus, the abstracted model for the hybrid system M is described in the intent domain I, which is much smaller compared to the original infinite-dimensional domain. Thus, the state space explosion problem is effectively addressed. Abstraction of transition: It should however be noted that in addition to the above abstraction of domain, the transition relations must also be abstracted to obtain an intent-based FSM. Such an abstracted intent-based FSM model M of the automation describes the evolution of flight intents of the automation, governed by the aircraft’s continuous states and the control inputs satisfying the guard condition as in Eq. (2), and can be verified using a discrete model-checker such as the NuSMV. The abstracted transitionion of transition: It should however be noted that in addition to the above abstraction of domain, the transition relations must also be abstracted to obtain an intent-based FSM. Such an abstracted intent-based FSM model M of the automation describes the evolution of flight intents of the automation, governed by the aircraft’s continuous states and the control inputs satisfying the guard condition as in Eq. (2), and can be verified using a discrete model-checker such as the NuSMV. The abstracted transition

Diagnostic Tool for Throughput Factor Analysis in En-route Airspace

Today’s National Airspace System (NAS) is approaching its limit to efficiently cope with the increasing air traffic demand. Next Generation Air Transportation System (NextGen) with its ambitious goals aims to make the air travel more predictable with fewer delays, less time sitting on the ground and holding in the air to improve the performance of the NAS. However, currently the performance of the NAS is mostly measured using delaybased metrics which do not capture a whole range of important factors that determine the quality and level of utilization of the NAS. The factors affecting the performance of the NAS are themselves not well defined to begin with. To address these issues, motivated by the use of throughput-based metrics in many areas such as ground transportation, wireless communication and manufacturing, this paper identifies the different factors which majorly affect the performance of the NAS as demand (split into flight cancellation and flight rerouting), safe separation (split into conflict and metering) and weather (studied as convective weather) through careful comparison with other applications and performing empirical sensitivity analysis. Additionally, the effects of different factors on the NAS’s performance are quantitatively studied using real traffic data with the Future ATM Concepts Evaluation Tool (FACET) for various sectors and centers of the NAS on different days. In this paper we propose a diagnostic tool which can analyze the factors that have greater responsibility for regions of poor and better performances of the NAS, thus providing vital information which can be used for suitable economic and environmental advantages.