Driving simulators are found in various areas of use (such as training, industrial prototyping, behavioral studies) (Slob, 2008). A driving simulator is a system that provides a coherent multi-sensory environment for a driver to perceive and control virtual vehicle movements (Kemeny and Panerai, 2003). The driving simulation offers substantial advantages, which are listed below:

The visual interface, which presents to the driver the current environment state in synthesized images.

These techniques made it possible for the driving simulators to have many advantages compared to real vehicles (De Winter et al., 2012), including

Controllability, reproducibility, and standardization: the repeatability of the situations and the control of the parameters (behavior of virtual traffic, weather conditions, etc.),

Gameplay is a term used to describe the interactive aspects of game design. This includes game mechanisms, rules, choices, facilities, difficulties, and progression in order to achieve a goal. All these parameters play an important role in designing a good video-game. The motivation comes by the fact that the game should keep the player engaged, by giving him some tasks that will be carried out in a short-time, while making the play easy to learn but challenging to master (Prensky, 2002).

The fun component represents an important factor in the training process. According to Michael and Chen (2005) fun is a result rather than an objective. The more the tasks handled are amusing, the more the person will be motivated to perform these tasks in a voluntary way. However, the voluntary factor does not present in the case of the serious games. On the other hand, this fact does not preclude that the activity provided could be pleasant or desirable, provided it does not impede the training process (Michael and Chen, 2005).

Different playful ways have been used frequently to transmit the knowledge. Edutainment generally corresponds to educating and entertaining at the same time. The initiatives of this field had a few success because they were seen as annoying (Susi et al., 2007); furthermore, the e-Learning paradigm, which means the remote training through new multimedia technologies, has had a relative failure. In this case, the information is available online but a lack of motivation prevents to complete all exercises.

Serious-games attract a larger audience due to their interactive nature. They are always related to simulation applications. The realistic representation of simulated environments enables more immersion, and consequently more motivation. This immersion facilitates a faster training and better information retention (Michael and Chen, 2005). This potential makes the player more involved than training in a classroom (often perceived as useless and tiresome). All in all, these specifications make serious-games an interesting alternative in several sectors.

Eco-driving is a driving behavior toward the environment. Thus, the drivers will contribute to an important reduction of fuel consumption without compromising the efficient movement. According to Wengraf (2012) «Eco-driving is a set of steps, techniques and behaviors that drivers can employ in preparation of the vehicle before a journey, in planning the journey, in modifying driving style during the journey and in reviewing trip data after the journey, that can, taken together, lead to savings (at times, significant ones) in terms of fuel usage, trip cost, emissions of CO₂ and other pollution, and levels of noise from vehicle use. These savings can be realized for relatively low cost compared to other kinds of efforts to reduce the environmental impacts of car use. In addition, trips made with eco-driving techniques can, in many cases, be linked to improved road safety; moreover, the use of such techniques does not increase journey times».

This type of driving presents several advantages (Ecodrive, 2016):

Safety ○

Improve road safety and

The factors that could influence the increase of fuel consumption according to Ericsson (2001) and af Wåhlberg (2007) are:

The driver’s behavior and knowledge: lack of information and no respect of the basic rules of eco-driving increase the energy consumption.

There are several studies that have investigated the effectiveness of eco-driving in CO₂ emissions (see Table 1). It is difficult to determine the real benefits of eco-driving because the basic conditions change from one study to another (vehicles, road, mathematical models used). Generally speaking, it can be said that the eco-driving can significantly affect the amount of energy and emissions and the efficient driving can reduce emissions by up to 10%. The fuel consumption from eco-driving is more significant in long distance compared to short or medium distance (Bozicnik and Hanzic, 2009). Many eco-driving programs have been implemented in the last 20 years in several countries (see Table 2).

Despite technological advances made in cars’ engines and especially in energy consumption, fuel consumption remains a considerable challenge in thermal cars. The adaptation of the existing system seems however difficult. According to Larsson and Ericsson (2009), the use of a resistant accelerator pedal beyond a certain level does not give any significant reduction of consumption. Also, many studies (Regan et al., 2006; Saint-Pierre and Ehrlich, 2008) show that the use of the Intelligent Speed Adaptation (Ericsson, 2001) does not bring any significant reduction of fuel consumption. Besides, a recent study (Azzi et al., 2011) has shown the effectiveness of visual and haptic assistance in eco-driving.

Many car manufacturers propose Ecological Driving Assistance System (EDAS). These assistance systems are mainly embedded devices aboard the vehicles that collect varied data from the driver’s behavior, the road, and/or the other vehicles. These systems use the whole information to analyze the driver’s behavior, in order to broadcast some crucial recommendations to foster self-learning in eco-driving. However, the use of these EDAS alone without motivation or sensitization does not provide any significant reduction of fuel consumption. The driving style and the driver behavior remain the important elements to be taken into account in the process of eco-driving (Ericsson, 2001).

In this context and in order to support the eco-driving training process, we have developed an approach that is based on the use of virtual reality and the serious game. The objective is to study the influence of a short training in eco-driving environment.

The scientific issues that are addressed in our approach are described below:

To validate the effectiveness of a short training;

Although this research is not related to the driving task, but the comprehension of this activity is essential in order to propose some guidance techniques with regard to respecting the multidimensional character of this task.

Driving is a complex activity; the driver must have a good reflex and makes the appropriate decisions in order to interpret one or more situations in an efficient way, which could change or degrade very quickly.

According to Michon (1985), the driving task is organized in three levels:

The strategic level is the highest level of the hierarchy. The tasks in this level require a cognitive effort. However, the drivers are not subjected to strong temporal constraints (for example, determining destinations and waypoints).

The driving activity asks for a very high sensory implication level. The principal senses requested during the driving task are (Gruening et al., 1998; Kemeny and Panerai, 2003):

The vision,

The driving task is a particular task which requires from the drivers an extremely cognitive effort. Taking into account this characteristic in the design stage of eco-driving guidance techniques is essential for avoiding interferences between the messages that are naturally used by the driver during the driving task and the messages that they are possible to use in the guidance techniques. It is important not to overload the driver’s cognitive capacity, nor to disturb the driver for ensuring the good comprehension of these techniques.

Thus, it is necessary to propose the guidance techniques in a way of taking into account these following criteria:

Interactivity,

Thereafter, the guidance techniques will be developed according to cognitive criteria as well as eco-driving criteria. These rules are described in Table 3.

For each rule we propose visual and auditory interactive guidance (see Table 4).

Moreover, an indicator permanently posts the CO₂ emission level, and a visual message “increase your speed” appears in the center of the screen in blue at the beginning of the courses, if the player speed is lower than 10 km/h at the speed of reference recorded by an expert in eco-driving. The dashboard includes always at least the following elements: an engine RPM, a digital speed meter, a gearbox ratio indicator and a GPS (Figure 1).

The system realized is integrated into eco² simulator. The hardware architecture is shown in Figure 2.

The eco² simulator (Figure 3) is composed of the following components:

An immersive visualization with three LCD monitors (32 inch diagonal, 100 degree field of view),

SCANeR II is a driving simulation system designed by Renault and published by Oktal. The product is based on OpenSceneGraph and is intended for automobile engineering. The main components in SCANeR II are (Oktal, 2016) as follows:

Models: dynamic vehicle, autonomous traffic, pedestrians, scripting.

The software is used around the operational processes of driving simulators, structured around five dedicated modes of the graphic interface (Oktal, 2016):

Terrain mode: road network creator RoadXML allowing the rapid creation of realistic road networks-useable directly in the simulation.

For the development of our guidance techniques, we have used SCANER development kit (SDK) that gives the possibility to enriching the system. We have developed modules and plugins (in C++ language) to integrate our guidance techniques and manage the serious game (Figure 4):

Administrator: graphical user interface used to manage the serious game (player, scenario, scoring, etc.),

A total of 72 drivers took part in the study (3 groups of 24 drivers), 45 men and 27 women, aged between 19 and 55 years (the mean age is 24 years). They have had their driving licenses for at least 1 year (36 years to the maximum and 5 years as average) and they drive an average of 9000 km per year. The participants were randomly assigned to one of the three groups. They were all remunerated for their participation (10 Euros or a USB key). The sample includes students (in majority) and non-students (parents, personnel of the university).

Ethical approval was waived because the study subjects’ identifiers were not used and the study protocol was not deemed to represent epidemiological or biomedical research. We made every effort to comply with data protection rules. Study participation was voluntary and will not induce any risk of distress or injury, physical or psychological to the subjects. All participants signed an informed consent form before entering the study. In the cover letter accompanying the questionnaire, it was made clear that participation was on a voluntary basis and that the results would be used for research purposes only.

The analysis performed on the CO₂ emission with an ANOVA. We noted a significant reduction in terms of CO₂ emission between the initial course and the final course (Figure 6), F(1,69) = 30.7, p < 0.0001. The interaction between the factor groups and the factor course (Scenario × Group) revealed a reliable interaction, F(3, 69) = 8.54, p < 0.001.

Post hoc testing (initial vs. final), reveals a:

Significant difference (reduction) at the group with lesson and guidance [F(1, 69) = 32.20, p < 0.0001].

Post hoc testing (G1 vs. G2 vs. G3), reveals a:

significant difference in CO₂ emission on the final course F(2,69) = 5.21, p = 0.007 and

The comparisons of the scores obtained between the “initial” and “final courses” show that all the rules are generally assimilated, except for the rule 2; the players did not really use the highest gear (see Table 6).

Detailed statistical analyses (linear regression) allow determining the relative contribution for each rule. Two important conclusions can be stated:

Rules 4 and 5 are the rules that seem to contribute more to the economy realized in terms of CO₂.