Table of Contents

Cover

Introduction

PART 1: Perceptual Capacities

1 Synchronization of Stimuli with Heart Rate: a New Challenge to Control Attentional Dissonances
1. 1.1. Introduction
2. 1.2. From human error to dissonance
3. 1.3. Cognitive conflict, attention and attentional dissonance
4. 1.4. Causes and evaluation of attentional dissonance
5. 1.5. Exploratory study of attentional dissonances
6. 1.6. Results of the exploratory study
7. 1.7. Conclusion
8. 1.8. References
2 System-centered Specification of Physico–physiological Interactions of Sensory Perception
1. 2.1. Introduction
2. 2.2. Situation-system-centered specification of a sensory perception interaction
3. 2.3. Physiology-centered specification of a sensory perception interaction
4. 2.4. System-centered specification of an interaction of sensory perception
5. 2.5. Conclusion
6. 2.6. References

PART 2: Cooperation and Sharing of Tasks

3 A Framework for Analysis of Shared Authority in Complex Socio-technical Systems
1. 3.1. Introduction
2. 3.2. From the systematic approach to the systemic approach: a different approach of sharing authority and responsibility
3. 3.3. A framework of analysis and design of authority and responsibility
4. 3.4. Management of wake turbulence in visual separation: a study of preliminary cases
5. 3.5. Conclusion
6. 3.6. References
4 The Design of an Interface According to Principles of Transparency
1. 4.1. Introduction
2. 4.2. State of the art
3. 4.3. Design of a transparent HCI for autonomous vehicles
4. 4.4. Experimental protocol
5. 4.5. Results and discussions
6. 4.6. Conclusion
7. 4.7. Acknowledgments
8. 4.8. References

PART 3: System Reliability

5 Exteroceptive Fault-tolerant Control for Autonomous and Safe Driving
1. 5.1. Introduction
2. 5.2. Formulation of the problem
3. 5.3. Fault-tolerant control architecture
4. 5.4. Voting algorithms
5. 5.5. Simulation results
6. 5.6. Conclusion
7. 5.7. References
6 A Graphical Model Based on Performance Shaping Factors for a Better Assessment of Human Reliability
1. 6.1. Introduction
2. 6.2. PRELUDE methodology
3. 6.3. Case study
4. 6.4. Conclusion
5. 6.5. Acknowledgments
6. 6.6. References

PART 4: System Modeling and Decision Support

7 Fuzzy Decision Support Model for the Control and Regulation of Transport Systems
1. 7.1. Introduction
2. 7.2. The problem of decision support systems in urban collective transport
3. 7.3. Montbéliard’s transport network
4. 7.4. Fuzzy aid decision-making model for the regulation of public transport
5. 7.5. Conclusion
6. 7.6. References
8 The Impact of Human Stability on Human–Machine Systems: the Case of the Rail Transport
1. 8.1. Introduction
2. 8.2. Stability and associated notions
3. 8.3. Stability in the human context
4. 8.4. Stabilizability
5. 8.5. Stability within the context of HMS
6. 8.6. Structure of the HMS in the railway context
7. 8.7. Illustrative example
8. 8.8. Conclusion
9. 8.9. References

PART 5 Innovative Design

9 Development of an Intelligent Garment for Crisis Management: Fire Control Application
1. 9.1. Introduction
2. 9.2. Design of an intelligent garment for firefighters
3. 9.3. Physiological signal processing
4. 9.4. Firefighter–robot cooperation, using intelligent clothing
5. 9.5. Conclusion
6. 9.6. References
10 Active Pedagogy for Innovation in Transport
1. 10.1. Introduction
2. 10.2. Analysis of a railway accident and system design
3. 10.3. Analysis of use of a cruise control system
4. 10.4. Simulation of a collision avoidance system use
5. 10.5. Eco-driving assistance
6. 10.6. Towards support for the innovative design of transport systems
7. 10.7. Conclusion
8. 10.8. References

Conclusion

List of Authors

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1. Examples of dissonances

Chapter 2

Table 2.1. Types of affordance for a physico-physiological interaction of audito...

Chapter 4

Table 4.1. IPLC-based matrix
Table 4.2. IPLC matrix for the T1 principle
Table 4.3. IPLC matrix for the A1 principle
Table 4.4. Specifications of the five HCIs according to the functions of [PAR 00...
Table 4.5. Composition of the participants according to the criteria “sex” and “...
Table 4.6. Number of participants per HCI/scenario
Table 4.7. Variables used in the experimental process
Table 4.8. Questionnaire G on situational awareness

Chapter 5

Table 5.1. Main causes of accidents in France in 2015 (source: ONSIR) – the tota...
Table 5.2. Vehicle parameters

Chapter 6

Table 6.1. Comparison of some of the main human reliability analytical methodolo...
Table 6.2. List of PSFs with definitions and quantification levels taken into ac...
Table 6.3. Defining the Human Failure Type Context for an HFE example
Table 6.4. Description of the HFE and the relevant context as well as the questi...
Table 6.5. Combination rules
Table 6.6. Direct belief structures for the HFE: from the equivalent R-PSF as id...
Table 6.7. Marginalization results on the HFE example n_HFE
Table 6.8. HFE and relevant procedures according to the investigation report on ...
Table 6.9. Factor identification for the definition of HFTC, corresponding to th...
Table 6.10. Context description and questions for HFE2 sent to the experts
Table 6.11. Direct belief structures for the PSFs in the case study, identified ...
Table 6.12. Midpoint of the probability interval for the (true) variable of HFE1...
Table 6.13. The pairwise distance metric d_J between the BPAs obtained for the “t...

Chapter 7

Table 7.1. Decision rule table

Chapter 8

Table 8.1. Probability of transitions between states

Chapter 10

Table 10.1. Results of the individual evaluation of the module
Table 10.2. Example of basic rules
Table 10.3. Validation example of rules and dissonances using CC

List of Illustrations

Chapter 1

Figure 1.1. Screen display of levels 3 and 4 and appearance of the visual and au...
Figure 1.2. Effect of the synchronous/asynchronous condition on errors
Figure 1.3. Effect of the level of mental demand (N1–N4) on errors
Figure 1.4. Relationship between condition and the level of mental demand
Figure 1.5. Effect of the level of mental demand on the Hr
Figure 1.6. Effect of the level of mental demand for the NASA-TLX (RTLX) scale
Figure 1.7a. Evaluation of the dimensions (VAS out of 100) as a function of the ...
Figure 1.7b. Evaluation of the dimensions (VAS out of 100) as a function of the ...
Figure 1.8a. Evaluation of valence, arousal and dominance (SAM out of 9) as a fu...
Figure 1.8b. Evaluation of valence, arousal and dominance (SAM out of 9) as a fu...
Figure 1.9. Example of genesis of attentional dissonances

Chapter 2

Figure 2.1. System context of the model-based interdisciplinary specification of...
Figure 2.2. Towards coupling between multidisciplinary situations of operation a...
Figure 2.3. Coupling relations between systems architecting levels. For a color ...
Figure 2.4. Operational situations of multidisciplinary specification of sensory...
Figure 2.5. System-thinking for system-centering the architecture. For a color v...
Figure 2.6. Cognitive and specifying interpretation of the coupling diagram. For...
Figure 2.7. Causal loop diagram of the targeted control situation. For a color v...
Figure 2.8. Stock-flow diagram of the targeted control situation system. For a c...
Figure 2.9. SysML activities diagram prescribing the targeted control situation ...
Figure 2.10. Elements of multidisciplinary knowledge of the mathematical theory ...
Figure 2.11. Schematic representation of circuits of different sensory perceptio...
Figure 2.12. Analogy of the behavior of the functional interaction with a thyris...
Figure 2.13. Elements of understanding of the operational interaction of targete...
Figure 2.14. Elements of understanding that center the interaction of sound sens...
Figure 2.15. Diagram of physiological requirements contextualizing the interacti...
Figure 2.16. Situation system-centered architecting specification of an interact...
Figure 2.17. Control-centered architecting specification refinements. For a colo...
Figure 2.18. Blocks of the Simulink diagram centering the interaction of sound s...
Figure 2.19. Scenarios of testing “human-centered intelligent measurement”. For ...
Figure 2.20. Structural model of orchestration of the specification of human-cen...
Figure 2.21. Trace of execution of the scenarios of system validation in silico ...

Chapter 3

Figure 3.1. Relations between the authority, responsibility and accountability
Figure 3.2. Dynamic relations between the notions of authority, responsibility a...
Figure 3.3. Example of characterization of the delegation of authority in the ca...
Figure 3.4. Application of the framework for analysis of responsibility and auth...

Chapter 4

Figure 4.1. Lyons’ models according to Debernard et al. [DEB 16]
Figure 4.2. The transparency principles associated with Lyons’ models
Figure 4.3. Extract of the analysis of the field of work. The blue lines show so...
Figure 4.4. Rasmussen’s double scale for changes of lane
Figure 4.5. DrSIHMI
Figure 4.6. Terrain used during the simulation. The journey in orange is the one...
Figure 4.7. Hypothesis tests to evaluate the impact of the factors I, S, SF and ...
Figure 4.8. Ranking of the I interfaces by each participant. For a color version...
Figure 4.9. Ranking of the interfaces in first position

Chapter 5

Figure 5.1. Statistics on causes of accidents by age group (source: ONSIR). For ...
Figure 5.2. Vehicle automation levels (source: Argus)
Figure 5.3. Fault-tolerant control architecture, based on voting algorithms
Figure 5.4. Longitudinal dynamics of a vehicle
Figure 5.5. Maximum likelihood algorithm
Figure 5.6. Weighted averages algorithm
Figure 5.7. History-based weighted average algorithm
Figure 5.8. Reliability variation depending on the distance, according to three ...
Figure 5.9. Fault emulation on different sensor measurements. For a color versio...
Figure 5.10. Speed profile of the leading vehicle
Figure 5.11. Inter-vehicle distance using the maximum likelihood voting (MLV) al...
Figure 5.12. Sensor transition using the maximum likelihood voting (MLV) algorit...
Figure 5.13. Inter-vehicle distance using the weighted averages (WA) algorithm. ...
Figure 5.14. Weights of the weighted averages (WA) algorithm. For a color versio...
Figure 5.15. Inter-vehicle distance using the history-based weighted average (HB...
Figure 5.16. Evolution of state indicators (HBWA). For a color version of this f...
Figure 5.17. Comparison of inter-vehicle distance using the three algorithms. Fo...
Figure 5.18. Reference speed comparison. For a color version of this figure, see...
Figure 5.19. Speed profile comparison. For a color version of this figure, see: ...
Figure 5.20. Acceleration profile comparison. For a color version of this figure...

Chapter 6

Figure 6.1. Overview of the PRELUDE methodology. Step 1: qualitative section tha...
Figure 6.2. A simple example and its associated variables modeled in a VBS
Figure 6.3. Using the EUAR HF study for identifying a safety critical context, a...
Figure 6.4. Graphical representation of the implementation of the HFE example in...
Figure 6.5. Results of the sensitivity analysis
Figure 6.6. Accident scenario: train speed as compared to distance from the acci...
Figure 6.7. Expert data (first three bars) and data obtained from the combinatio...
Figure 6.8. (a) VBS model of HFE1 and its HFTC; (b) VBS model of HFE2 and its HF...
Figure 6.9. Upper and lower limits for the actual state of an HFE: different mod...
Figure 6.10. Results of the PSF sensitivity analysis in the context of HFE2 and ...
Figure 6.11. The organizational chart of the PRELUDE methodology

Chapter 7

Figure 7.1. Two hubs in Montbéliard’s transport network
Figure 7.2. Regulation of an urban transport network
Figure 7.3. Fuzzy decision support model (FDSM)
Figure 7.4. Fuzzification process
Figure 7.5. Membership function for the advance/delay of buses.VL: very late; L:...
Figure 7.6. Membership function of the passenger flow. N: negligible; M: medium;...
Figure 7.7. Membership function of the time of day. RH: rush hour; CT: team chan...
Figure 7.8. Membership function of the bus slowdown. Tt: theoretical time; Tr: r...
Figure 7.9. Defuzzification process
Figure 7.10. Calculation of the center of gravity for example no. 1. G₀: center ...
Figure 7.11. Calculation of the center of gravity for example no. 1
Figure 7.12. Calculation of the center of gravity for example no. 2
Figure 7.13. Calculation of the center of gravity for example no. 3
Figure 7.14. Calculation of the center of gravity for example no. 4
Figure 7.15. Graph of the substitution decision. For a color version of this fig...
Figure 7.16. Graph of the transshipment decision. For a color version of this fi...
Figure 7.17. Graph of the connection decision. For a color version of this figur...

Chapter 8

Figure 8.1. Regulation loop: closed-loop control system
Figure 8.2. Constituents and characteristic parameters of the HMS in the context...
Figure 8.3. Closed-loop human–machine system
Figure 8.4. The HMS structure. For a color version of this figure, see: www.iste...
Figure 8.5. Supervision module
Figure 8.6. Human operator model
Figure 8.7. Modeling example
Figure 8.8. The COR&GEST platform
Figure 8.9. Example of sensor’s outputs. For a color version of this figure, see...
Figure 8.10. Evolution of the train speed compared to the instructions given. Fo...
Figure 8.11. Occurrence of faults
Figure 8.12. Driver’s estimated emotions
Figure 8.13(a). Horizontal movement of the gaze
Figure 8.13(b). Horizontal movement of the gaze

Chapter 9

Figure 9.1. An example of smart clothing
Figure 9.2. Architecture of the proposed portable smart system
Figure 9.3. Design of a “second skin” intelligent garment
Figure 9.4. The intelligent garment prototype
Figure 9.5. Original signal and its extracted characteristics (heartbeat in red ...
Figure 9.6. An example of detection of gravity peaks for a period of 20 seconds
Figure 9.7. RMSSD: root mean square of the successive differences, which means t...
Figure 9.8. SDANN (standard deviation of the 5-minute average NN intervals): sta...
Figure 9.9. SDANN: acquisition of performance data during the physical resting p...
Figure 9.10. A Mindstorms NXT robot with a smartphone interface
Figure 9.11. Human supervisor interface: supervision and control robots
Figure 9.12. Human supervisor interface: supervision of the hostile environment....

Chapter 10

Figure 10.1. Real scenario case study
Figure 10.2. Simulation case study using MissRail®. For a color version of this ...
Figure 10.3. Serious game for rail eco-driving tasks
Figure 10.4. Consumption results of the eco-driving system based on mirror learn...
Figure 10.5. Example of parameters for innovative design
Figure 10.6. Example of the implementation of assisted levitation

First published 2019 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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The rights of Frédéric Vanderhaegen, Choubeila Maaoui, Mohamed Sallak and Denis Berdjag to be identified as the authors of this work have been asserted them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2019937198

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ISBN 978-1-78630-422-3

Introduction

Sociotechnical systems have been studied under different names: human–machine systems, multi-agent systems, systems of systems, complex systems, cyber-physical and human systems. Research studies on such systems encounter similarities in the problems associated with their design, analysis or evaluation. So, is it their level of complexity that justifies human presence or is it the presence of humans that makes them complex? Two themes of research are currently ongoing: one that studies human behaviors to gain a better understanding of their complexity and to provide them with better assistance, and another one that focuses on automation in order to avoid this complexity.

The chapters of this book bring together the two types of contributions and result from the work of various research groups:

– the Regional Federation for Research in Integrated Automation and Human–Machine Systems (GIS GRAISyHM – Groupe d’Intérêt Scientifique de Recherche en Automatisation Intégrée et Systèmes Homme-Machine) that encompasses all laboratories in automation, computer engineering and signal processing of the Hauts-de-France region;
– the Working Group in Automation of Human–Machine Systems (GT ASHM – Groupe de Travail Automatisation des Systèmes Hommes-Machines) within the Research Group on Modeling, Analysis and Control of Dynamic systems (GDR MACS – Groupe de Recherche en Modélisation, Analyse et Conduite des Systèmes dynamiques) of the French National Centre for Scientific Research;
– the research communities from the conference on Ergonomics and Advanced Data Processing (ERGO-IA – Ergonomie et Informatique Avancée) that bring together competences in the field of cognitive psychology and cognitive ergonomics, human–machine interactions and systems engineering.

The challenges described in this book are grouped into five parts: perceptual capacities, cooperation and task sharing, system reliability, decision modeling and supports, and innovative design. Each part includes two chapters that present key contributions responding to the challenges of automation in sociotechnical systems.

Part 1 proposes challenges in relation to the perceptual capacities of a sociotechnical system. Chapter 1 proposes a new approach to detect perceptual blindness. It is based on the synchronization of events such as the occurrence of visual and auditory alarms with heartbeats. Chapter 2 is a multimodal interdisciplinary approach to engineering of multidisciplinary systems. It explains the advantage of multiphysical systems of interaction to facilitate the sensorial perception of human operators in exploratory contexts of industrial maintenance or supervision.

Part 2 focuses on the use of automated systems and the role of human operators interacting with these systems as part of human–machine cooperation and the task sharing. Chapter 3 proposes a new context to analyze the trading of authority between humans and machines. This is divided into different levels such as delegation, distribution, sharing or contractualization, and is associated with the definition of degrees of automation of new control or supervision systems in the field of air traffic. Chapter 4 develops the concept of transparency in the design of autonomous vehicles. Application of this concept allows for an easier understanding of the operation of autonomous systems and an increased awareness of the current situation among drivers.

Part 3 is based on the principles of technical or human reliability. Chapter 5 is a feasibility assessment of the design of an autonomous vehicle by taking into account the probabilities of possible sensor failures that give redundant information about the environment. Chapter 6 gives details of the new PRELUDE method based on a graphical model for the calculation of human reliability from various factors and from the acquisition of expert knowledge.

Part 4 includes two contributions to modeling of sociotechnical systems and decision support. Chapter 7 proposes a fuzzy model as an aid to decision construction in an uncertain environment, applied to the command, control or regulation of transport systems. In Chapter 8, the concept of human stability is associated with the resilience of sociotechnical systems. Modeling of this, and demonstration of its advantage, are illustrated in the context of a simulation of railway operations.

In Part 5, examples of innovative design are proposed. An item of smart clothing is developed in Chapter 9. Its use is illustrated in a context of management of crises with the participation of rescue teams. Chapter 10 describes active pedagogy modules dedicated to innovation in transport.

Introduction written by Frédéric VANDERHAEGEN, Choubeila MAAOUI, Mohamed SALLAK and Denis BERDJAG.

Automation Challenges of Socio-technical Systems

Introduction

PART 1
Perceptual Capacities