Petri net-based modelling of human–automation conflicts in aviation

Analyses of aviation safety reports reveal that human–machine conflicts induced by poor automation design are remarkable precursors of accidents. A review of different crew–automation conflicting scenarios shows that they have a common denominator: the autopilot behaviour interferes with the pilot's goal regarding the flight guidance via ‘hidden’ mode transitions. Considering both the human operator and the machine (i.e. the autopilot or the decision functions) as agents, we propose a Petri net model of those conflicting interactions, which allows them to be detected as deadlocks in the Petri net. In order to test our Petri net model, we designed an autoflight system that was formally analysed to detect conflicting situations. We identified three conflicting situations that were integrated in an experimental scenario in a flight simulator with 10 general aviation pilots. The results showed that the conflicts that we had a-priori identified as critical had impacted the pilots' performance. Indeed, the first conflict remained unnoticed by eight participants and led to a potential collision with another aircraft. The second conflict was detected by all the participants but three of them did not manage the situation correctly. The last conflict was also detected by all the participants but provoked typical automation surprise situation as only one declared that he had understood the autopilot behaviour. These behavioural results are discussed in terms of workload and number of fired ‘hidden’ transitions. Eventually, this study reveals that both formal and experimental approaches are complementary to identify and assess the criticality of human–automation conflicts. Practitioner Summary: We propose a Petri net model of human–automation conflicts. An experiment was conducted with general aviation pilots performing a scenario involving three conflicting situations to test the soundness of our formal approach. This study reveals that both formal and experimental approaches are complementary to identify and assess the criticality conflicts

Towards human operator “state” assessment

This paper focuses on an approach to estimate the symbolic “state” and detect the attentional tunneling of a human operator in the frame of a human-robot mission. The symbolic “state” results from a fuzzy aggregation of the operator's gaze position and heart rate

What the heck is it doing? Better understanding human-machine conflicts through models

This paper deals with human-machine conflicts with a special focus on conflicts caused by an “automation surprise”. Considering both the human operator and the machine autopilot or decision functions as agents, we propose Petri net based models of two real cases and we show how modelling each agent’s possible actions is likely to highlight conflict states as deadlocks in the Petri net. A general conflict model is then be proposed and paves the way for further on-line human-machine conflict forecast and detection

Formal Detection of Attentional Tunneling in Human Operator-Automation Interactions

The allocation of visual attention is a key factor for the humans when operating complex systems under time pressure with multiple information sources. In some situations, attentional tunneling is likely to appear and leads to excessive focus and poor decision making. In this study, we propose a formal approach to detect the occurrence of such an attentional impairment that is based on machine learning techniques. An experiment was conducted to provoke attentional tunneling during which psycho-physiological and oculomotor data from 23 participants were collected. Data from 18 participants were used to train an adaptive neuro-fuzzy inference system (ANFIS). From a machine learning point of view, the classification performance of the trained ANFIS proved the validity of this approach. Furthermore, the resulting classification rules were consistent with the attentional tunneling literature. Finally, the classifier was robust to detect attentional tunneling when performing over test data from four participants

An Error Model of a Complementary Filter for use in Bayesian Estimation - The CF-EKF Filter

It is well known that stand-alone inertial navigation systems (INS) have their errors diverging with time. Consequently an upper bound on the duration of INS systems precludes their use in low-cost micro unmanned aerial vehicles. The traditional approach for solving this matter is to resort to aiding devices, e.g., global navigation satellite system (GNSS) receivers, sighting devices, etc. Two philosophies have been extensively applied to perform data fusion: extended Kalman filtering (EKF) and complementary filtering (CF). Previous work in the literature showed that the computationally less expensive CF can be robustly applied to attitude estimation using low-cost sensors and achieve performance that is comparable to that of a full EKF. However, performance is degraded by vehicle manoeuvres and no measurement on estimate uncertainties is given. Furthermore, a large number of sensors makes it impracticable for optimal tuning of the CF. The present work lays the foundation for sensor filtering that employs the CF for attitude estimation by means of a magnetometer as an external aid, and an EKF for additional sensors integration. The main feature of this architecture is the possibility of deployment in a distributed multi-platform system and implementation of fault isolation by running the CF stage in a separate low-throughput reliable machine for stand-alone degraded mode operation. A case study is performed on synthetic data from inertial, magnetic and GNSS sensors

An Error Model of a Complementary Filter for use in Bayesian Estimation - The CF-EKF Filter

It is well known that stand-alone inertial navigation systems (INS) have their errors diverging with time. Consequently an upper bound on the duration of INS systems precludes their use in low-cost micro unmanned aerial vehicles. The traditional approach for solving this matter is to resort to aiding devices, e.g., global navigation satellite system (GNSS) receivers, sighting devices, etc. Two philosophies have been extensively applied to perform data fusion: extended Kalman filtering (EKF) and complementary filtering (CF). Previous work in the literature showed that the computationally less expensive CF can be robustly applied to attitude estimation using low-cost sensors and achieve performance that is comparable to that of a full EKF. However, performance is degraded by vehicle manoeuvres and no measurement on estimate uncertainties is given. Furthermore, a large number of sensors makes it impracticable for optimal tuning of the CF. The present work lays the foundation for sensor filtering that employs the CF for attitude estimation by means of a magnetometer as an external aid, and an EKF for additional sensors integration. The main feature of this architecture is the possibility of deployment in a distributed multi-platform system and implementation of fault isolation by running the CF stage in a separate low-throughput reliable machine for stand-alone degraded mode operation. A case study is performed on synthetic data from inertial, magnetic and GNSS sensors

Conflict prediction in human-machine systems

Prédiction des conflits dans le systèmes homme-machine. Le travail fait partie de la recherche consacrée à des problèmes d'interaction homme-machine et aux conflits entre l'homme et la machine qui pourrait découler de ces situations.Conflict prediction in human-machine systems. The work is part of research devoted to problems of human-machine interaction and conflict between human and machine which may arise from such situations

BOREAL-A Fixed-Wing Unmanned Aerial System for the Measurement of Wind and Turbulence in the Atmospheric Boundary Layer

International audienceAn instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed-wing UAV built by BOREAL company, which weighs up to 25 kg (5 kg of payload) and has a wingspan of 4.2 m. With a light payload and optimal weather conditions, it has a flight endurance of 9 h. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s-1, which is fast enough to capture turbulence fluctuations: a GPS-inertial measurement unit (IMU) platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60 m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10 Hz and corresponding to a spatial resolution to the order of 3 m are hereby presented