10.1. Introduction

Classical pedagogy places the teacher in front of students whose knowledge levels may be heterogeneous. In this configuration, some of them remain passive and follow the course without really interacting with the teacher. In order to overcome these setbacks, methods for active pedagogy have been created so as to engage learners in their learning process, at their own pace. These methods include, for example, reverse pedagogy [GUI 17] and problem learning [GOO 05].

Reverse pedagogy involves solving problems through support by courses or through information provided by the teacher on demand. Learning through problems or through projects enables students to process and solve fictional problems or scenarios, thus putting their knowledge into practice and progressively completing the learning process. These techniques can be carried out remotely or face-to-face with the teacher, who can be solicited by the students so as to facilitate or to guide their work. These techniques encourage students to become actors of their learning process [LEB 07, WAL 17].

In this pedagogical framework, metaphors are often employed for making understanding easier or for illustrating the use of a concept [LYN 17, THI 17, DRO 18]. Sometimes, visual aids make it possible to establish a direct association between an icon or a drawing and the problem at hand. In augmented virtual reality, the metaphor for designing decision support systems based on an enhanced dynamic view helps the user to naturally understand the purpose or interest of such assistance [GEO 11, PHA 16]. Two metaphors are used in the final examples of active pedagogy presented in this chapter: the mirror effect, for illustrating the implementation of the principle of human-assisted automation as an optimal support for eco-driving; and the flying carpet, as a means for designing innovative transport.

This chapter introduces the results of active pedagogy examples related to learning through problems or through projects, and is dedicated to the innovation of transport systems. The first two examples concern risk and accident analysis through the use of support systems for improving design tools destined to the prevention, recovery or restriction of adverse events. Such active pedagogy modules were implemented in two master’s degree classes from the University of Valenciennes between 2016 and 2018. Teaching was based on the distance learning modules developed for the “Railway Engineering and Guided Systems” specialization in Valenciennes, within the framework of the UTOP project (Université de technologie ouverte pluripartenaire) belonging to the IDEFI program (Initiatives d’excellences en formations innovantes). The third and fourth examples incorporate elements from the first two studies and also implement digital tools for simulating scenarios within the framework of a collaborative project involving five students who followed the first year of the master’s degree in 2017. The last example proposes a global approach for the innovative design of transport systems.

10.2. Analysis of a railway accident and system design

The problem was based on an accident report from the Accident Investigation Bureau [BEA 05]. The facts were presented to the student groups as follows:

“After leaving the warehouse where he had spent the night and loaded his trailer, a truck driver began his 200-km non-stop journey. Unfortunately, the truck containing gas cylinders broke down and remained stuck on a railway track, since the braking system was faulty. This road used to be frequented by heavy trucks. The driver in the blocked truck asked for help from another driver who suggested making a phone call. A few seconds before the closing of the level crossing (LC) announcing the arrival of a train, he tried to communicate with the control center (CC) by using the intended lane phone. During the communication that was very short (six seconds), interference made it impossible for the CC staff to understand the situation, and consequently the CC staff did not sound the alarm.

A TERYY train coming out of a curved track section was approaching the LC along a straight line track at 50 km/h. As he perceived the lorry stuck on the lane, the driver of the TERYY immediately triggered the emergency braking and transmitted the warning light (WL) and the radio warning (RW), inviting all other train drivers in the area to instantly stop due to force majeure. He managed to stop his train before the LC. There, he found that the blocked truck did not hinder the passage of his TERYY, but that of the TERXX which was journeying at 100 km/h on a parallel track. When he discovered the TERXX collision with the truck, he warned the CC by radio.

The driver of the TERXX also saw the truck stuck on his path at the LC. His train was rolling at 100 km/h when he crossed the TERYY. He then activated the emergency stop button and ordered the lowering of the pantographs. He did not issue the RW because it has just been triggered by the TERYY he had overtaken and which had stopped before the LC. Just before the impact with the truck, the driver left his cabin and entered the passenger car. He warned the travelers about the imminent shock with the truck and asked them to get back to their seats, to remain seated, to hold themselves strongly to the seats and to wait for instructions. The shock took place at 40 km/h and the train pushed the immobilized truck a few meters before stopping”.

This scenario was analyzed by groups of three to five people. The exercise aimed to identify the human, technical and organizational factors involved throughout the course of the accident, setting up appropriate fault trees in order to provide a generic representation of the accident and the post-accident and suggesting technical recommendations. The search for information or documentation regarding the different elements of this problem could be carried out in a library, on the Internet or through interaction with the teacher. Articles on the evaluation of human errors, risk analysis and the modeling of human behavior were made available for consultation in case of need [VAN 99a, VAN 03, QIU 17, RAN 17, VAN 17a].

At the end of the module, the groups made a presentation of their results and the professor offered a synthesis highlighting the theoretical aspects that should be retained. The removal of the LC through a tunnel or a bridge, the earlier lowering of the barriers (i.e. the lengthening of the release time) or the evacuation of passengers via the train’s rear were examples of organizational recommendations. Regarding technical recommendations, proposals for innovations on how to deal with such an accident were numerous. For example, students focused on how to avoid the truck, divert the train or reduce the risk of explosion and potential fires following the collision. The following proposals were made:

• – proposal of a system for emptying or evacuating the truck’s tank. The aim was to control one of the causes of a potential post-accident, that is, the fire;
• – proposal regarding a fire insulating system on the train or on the truck. Fire confinement could rely on specific materials or foams, for example;
• – proposal regarding a vehicle immobilization detector at the railway crossing. This system could be based on truck or car weighing tools (e.g. weighbridges or vehicle scales);
• – proposal for a motion detector by cameras. Progress in the field of image analysis has made it possible to specify an immobilized vehicle detection system at an LC thanks to the use of cameras;
• – proposal of a track occupancy detection system by drones. This system is based on the previous one, but instead of implementing fixed cameras on the railway site, these are mobile cameras, embedded on drones. The supervision of the traffic flow could thus be organized with the support of these devices in order to detect incidents on the LCs or along the tracks;
• – proposal of a treadmill for evacuating immobilized vehicles. This proposal studied several evacuation configurations based on the feasibility of integrating a treadmill across railways;
• – proposal of an evacuation winch for immobilized vehicles. This system was inspired by container landing machines on the docks of harbors;
• – proposal of LC upstream and downstream clearance tracks in case of collision. Depending by train speed and time constraints, the drivers could manipulate switches from their cab to deflect their train when approaching the LC.

At the end of the module, a questionnaire was offered to the students in order to assess the interest of this work (Table 10.1).

A total of 40 answers were collected. For a large number of students, the way in which this module was managed smoothed individual learning and favored a better understanding of the problem and the sharing of knowledge. This learning mode was considered useful. Its unfolding and the teamwork were also appreciated. For a smaller majority of students, the study of the problem enabled them to be autonomous and free when choosing which approach to follow. As regards the availability of course materials before the start of the module, the answers were divided, revealing a disparity between the choices and the preferences of each individual.

10.3. Analysis of use of a cruise control system

This module was divided into three sections: a collective reflection in small groups of three to five people, an individual section based on the same work support and another group activity for working on the specification of automatic detection and simulation tools for identifying intention conflicts (or dissonances) from basic rules. The first section was to prompt reflection concerning the rules of use and functioning of the cruise control (CC) system, the rules of the manual vehicle’s speed control, the rules of an aquaplaning manual control and the rules of the manual control for optimizing fuel consumption vehicles with a combustion engine. Table 10.2 is an example illustrating the production of these rules.

Groups could get documentation about the CC functioning modes regarding speed control in relation to a given instruction managed by the driver. The first section introduced the principles of group knowledge representation. The second section relied on basic common rules (Table 10.2).

These rules were trivial and implemented behavioral models associated with intentions, integrating a predicate and a conclusion following three parameters: the action to be performed, the object associated with the action and the actor. Two actors were identified: CC and the driver. The concept of dissonances introduced in [VAN 14a] was outlined and students were encouraged to identify them among the suggested rules. In order to help students during the exercise, a list of dissonances (i.e., A1, A2, C1, I1, I2, I3 and I4) based on those identified in [VAN 17a, VAN 17b] was suggested in order to continue the module (Table 10.3).

Two affordances A1 and A2, a contradiction involving the same actor and four interferences involving two different actors were suggested for evaluation. The A1 and A2 affordances referred to the abandonment of the CC “+” and “-” buttons for increasing or decreasing the speed setpoint except for increasing and decreasing the current speed. Thus, the CC can be used as an acceleration and deceleration system. Contradiction C1 was a conflict between rules R2 and R9 for which the driver could choose between two opposing actions: to brake or not to brake under particular circumstances of an aquaplaning occurrence and the intention to disable the CC, for example. Interference involved opposite actions between CC and the driver. Thus, I1 corresponded to a potential conflict between rules R8 and R10. The effect of aquaplaning could make the CC system miss the speed measurement, which may decide to accelerate if the current speed was considered to be lower than the setpoint speed. However, rule R10 specified not accelerating as a means for controlling aquaplaning. Interferences I2, I3 and I4 were related to behavior associated with uphill or downhill routes. Interferences appeared between CC actions and the driver’s intentions between rules R7 and R13 (i.e. I2), R8 and R14 (i.e. I3), and R7 and R15 (meaning I4).

Overall, the 33 surveyed master’s degree students fully agreed or mostly agreed with all the suggested dissonances, showing high or average certainty levels in their answers. The last section involved becoming acquainted with the knowledge about deductive, inductive and abductive human reasoning and adapting these to the implementation of automatic dissonance detection tools stemming from basic rules. Students could rely on the specifications given in [JOU 03, VAN 04, VAN 16a]. The implementation of these systems made it possible to automatically identify the contradictions or interferences in Table 10.3 or to determine new ones. For example, the same reasoning associated with interference I1 persisted when the current speed, which was initially equal to the setpoint speed, increased while aquaplaning. In fact, the CC system might decide to brake the engine speed by applying rule R7, whereas rule R9 did not require braking.

Dissonance simulation deduced from dynamic models and vehicle adhesion, under aquaplaning and CC activation conditions for example, could not be performed due to shortage of time. Therefore, the module had to be adjusted in order to implement this section.

However, the module made students aware of the uses of automated systems such as CC and of how to determine appropriate specifications for implementing new technical systems capable of acknowledging the risks of common dissonances. In addition, this module made it possible to implement practical knowledge regarding the development of dissonance diagnosis systems and their associated risks.

10.4. Simulation of a collision avoidance system use

In this exercise, the aim was to develop simulation media for detecting the risks associated with specific situations. A group of five master’s degree students had been working on this project since 2017 [HAM 18]. It was inspired in the simulation modules implemented on the MissRail® platform (a multi-function, multiuser and multimodal platform designed for railway training and research) [VAN 14b] and COR&GEST cyber-physical COR&GEST medium (rail driving and railway supervision) [VAN 12], developed at the University of Valenciennes.

In this way, starting from the example in Figure 10.1, risk analysis was to be carried out, following the basic traffic regulations.

Field observation was easily achievable since this junction is placed near the University of Valenciennes. Based on the previous problem about dissonance detection of operational rules and system use, the analysis involved identifying the potential risks associated with predefined scenarios. Once these risks were identified, they had to be analyzed following the parameters of the MissRail® platform of the University of Valenciennes. For example, Figure 10.2 illustrates a simulation example obtained thanks to this platform. It shows the risk of collision between a vehicle and a tram after having respected the green light.

Traffic density forced the driver to stop in order to avoid a frontal collision with another vehicle. Therefore, the stop was made on the tramway’s tracks. Depending on the waiting time, the traffic light turns red, announcing the imminent arrival of a tram. A collision was possible between the immobilized vehicle on the rails and the tramway. Another analysis was elicited regarding a vehicle equipped with an ACC-type collision avoidance system in the same circumstances. Simulation displayed the same potential collision scenario.

Thus, this exercise made it possible to sensitize students about the design of media for anticipating potential risks in a normal situation while complying with driving regulations (e.g. complying with the green light) and for designing tools for helping drivers acknowledge such constraints.

10.5. Eco-driving assistance

The results of this apprenticeship training module through projects were detailed in [HOM 18]. This exercise was integrated into the project of the group of five students discussed in the previous section. It was inspired by the concept of mirror learning, developed in [VAN 16b] for specifying and testing a railway eco-driving system. Unlike computer-assisted human activities, this medium makes it possible to implement human-assisted automation, introduced in [VAN 17b], applying the mirror effect metaphor to the learning process. This represents a new principle of augmented automation complementing technical support knowledge by taking into account the real human activities. Learning algorithm was suggested to the students in order to establish a first state of the art and to determine the algorithms that should be retained or adapted [POL 12, ENJ 17].

A serious game-type simulator was developed and two mirror effects were suggested: the selective mirror, in order to identify what should be kept as knowledge for optimizing energy consumption in real time, and the deforming mirror, so as to deform the common knowledge concerning predefined thresholds after each simulation, so as to discover and retain potential new local optima (Figure 10.3).

An interface, that is, a keyboard, a control box, a pedal, etc., helped the driver to position the manipulator in order to tow or to brake the train. The desired position of the manipulator was also indicated as advice to follow, as well as the actual position of the manipulator, which was supposed to match the setpoint, to ensure the optimization of power consumption.

Safety regarding the respect of speed limitation and acceleration and deceleration minimums was considered a priority. Train timetable respect was integrated into the knowledge optimization process concerning significant delays. Thus, during the first experiment by driver 1, no instructions were available, and future instructions were progressively calculated and optimized following subsequent experiments done by the other drivers (Figure 10.4).

Six subjects tested the platform. The first one had no instructions, but his behavior made it possible to initialize the instructions for the following experiment. The overall consumption of the second to the sixth subject could thus be improved by taking into account some of the optimal behaviors of each of them. Therefore, for these last five subjects, the middle histogram shows actual consumption. Consumption associated with the advice to be followed during the experiment is shown on the histogram on the left. The histogram on the right corresponds to the new instructions identified by the mirror system. The right histogram of an N subject corresponds to the left histogram of subject N + 1. For subject No. 6, actual consumption was important because the subject rarely respected the suggested advice, which hindered optimizing the initial instructions to significantly improve consumption for upcoming eco-driving system uses.

The perspectives of this work aimed to integrate the mirror effect learning module within a complete network involving several trains. The goal would then be to optimize overall power consumption by modulating speed instructions in real time and ensuring that schedules and passenger comfort are respected. The MissRail® platform [VAN 14b] allows for such a configuration of several trains on the same network, generating driving tasks in automated mode or in manual mode.

10.6. Towards support for the innovative design of transport systems

New design ideas for transport systems such as flying cars [DUC 18, FON 18], supersonic trains [ELH 17, RAY 18] or futuristic bicycles [HER 16, CAC 18] have demonstrated recent technological breakthroughs in the world of transport. In return, autonomous car accidents have reminded us about the important role of human factors in transport safety [GAV 17, LEC 18, ROZ 18]. Finally, in the face of the numerous controversies regarding the study of human factors [VAN 18a], this module proposed a new global approach for the proposal of an innovative transport system, abstaining from technical constraints. This aim is currently being achieved and intends to raise the awareness of learners in search for innovation in transport, with the possibility of conceiving the unthinkable or the impossible. It appeals to the imagination and creativity of students and is currently offering two levels of study. The first one concerns the proposal of automated systems for assessing the cognitive state of a human operator. The second one refers to the proposal of an assisted levitation system.

Figure 10.5 shows examples of parameters that can be taken into account for the design of transport systems:

• – the materials used for the transport system;
• – the laws of physics relating to different classical variables such as temperature or pressure;
• – smart technologies;
• – propulsion methods;
• – the behaviors of the gases used or produced;
• – human characteristics.

In the context of the introduction of autonomous vehicles, it is often desirable for the driver to remain vigilant and attentive, in order to be able to identify all the possible drifts of automated systems. Different media can then be developed while activating support systems based on a calculated or subjective estimate of the human workload, attention or vigilance, for example.

Based on bibliographic references made available to the students, a feasibility study is required for specifying such automated evaluation systems. For example, the idea was to develop a load estimator based on the functional or temporal requirements of tasks to be carried out [VAN 94, VAN 99b], an attention estimator based on the synchronization of events with heart rate [SAL 16, VAN 19] or an intention estimator based on water crystals placed near human operators [RAD 06]. The algorithms for real-time estimation of task requirements are offered to the students. Students could implement or adapt them and could validate them starting from an experimental protocol involving several subjects within a vehicle driving situation. As regards the attention estimator, students have to conceive a detection system for synchronizing the occurrence of dynamic events with the heart rate and then have to study the impact of such synchronization on the risk of making perceptual errors. Finally, the last example involving the estimation of intention was based on an original and surprising hypothesis, studied in [RAD 06]: it might be possible to determine the positive or negative intentions of a human being from the analysis of distilled water crystals placed nearby. For positive intentions associated with emotions like joy or good mood, the frozen and electronically enlarged water drops show structured geometric shapes, whereas in the presence of negative intentions associated with emotions such as anger or hate, crystals are destructured. As barometric statuettes that change color regarding the weather, this exercise aims to establish the functional specifications of a real-time system capable of determining the positive or negative state of surrounding intentions thanks to the mechanism of water crystal analysis presented in [RAD 06].

The second level of study intends to apply the metaphor of the flying carpet for assisted levitation. Figure 10.6 gives an example of a possible configuration. The material used may be derived from recent research such as graphene [TUR 17, BLA 18], and the technology may be assisted by a gas that is lighter than air, such as helium. Drones aid the system’s vertical movement or ascent. Depending on the overall weight of the set, levitation can be facilitated by managing the gas placed under the cockpit and handled by a human operator from a control panel. The exercise therefore consists of carrying out the technical feasibility study of such a system.

10.7. Conclusion

This chapter has offered examples of active pedagogy in which students were actively involved in the training modules. These were oriented towards the design of innovative transport systems. In stage one, the first two examples referred to two awareness modules regarding the risks of operations or uses of systems and took place during two master’s degree courses from the University of Valenciennes. The first one concerned the analysis of an accident and contributed to the design of collision avoidance systems. The second one was related to the analysis of the use and operation of a support system, by identifying intention conflicts, known as dissonances. Dissonance analysis was intended as a contribution to the design of specific knowledge that could be implemented in future driver assistance tools, for example. The third and fourth examples applied the principles of apprenticeship training through projects and were the result of work done by a group of five first-year master’s degree students. They were related to the simulation of an accident based on the concept of dissonance and the design of support tools based on the principle of human-assisted or human-augmented automation. The last example offered a new approach for the innovative design of a driving support system based on the assessment of the driver’s cognitive state and of transportation systems without imposing any technical constraints.

The first four examples gave satisfactory results and revealed the students’ interest in active pedagogy. They enhanced the learner’s initiative while favoring knowledge enrichment and collaborative work.

These results are important input points for future research on the development of pedagogical skills to be implemented in support systems. They will be exploited within the framework of the CONPETISES regional project (in French, “Contrôle pédagogique de tâches de conduite par systèmes automatisés”, or “Pedagogical Control of Driving Tasks by Automated Systems”) financed by the Hauts-de-France region and supported by GIS GRAISyHM (Group of Scientific Interest in Integrated Automation and Human–machine Systems).

The modules proposed in this chapter not only helped learners become aware of design-related research issues in automated systems in transport, but also helped them to identify and assess the cognitive biases associated with their use. For example, they justified the interest of studying important challenges such as the placebo or nocebo effects of automation, as well as potential opportunities and threats of automated tool use [VAN 18b]. Moreover, compared to the results obtained in [VAN 19], they made it possible to debate about a new principle to be applied during the design, analysis or assessment of socio-technical systems: what the user is looking at does not necessarily match what they have in mind. Finally, they highlighted other concepts considering the analysis or evaluation of human activities, as a dynamic support for automated systems. Thus, automation can be assisted in real time by human decisions, and conversely, these decisions can be optimized using technical systems [VAN 17b]. This requires in-depth studies on symbiosis between humans and machines in socio-technical systems based on technical, human, organizational, environmental, social, legal or ethical criteria.