Development of an Eco-Driving Simulation Training System with

Natural and Haptic Interaction in Virtual Reality Environments

Konstantinos Gardelis, Aris S. Lalos and Konstantinos Moustakas

Department of Electrical and Computer Engineering, University of Patras, Greece

Keywords:

Eco-Driving, Virtual Reality, Augmented Reality, Gamiﬁcation, Unity, Simulator, Training.

Abstract:

Road transport is one of the major causes of the environmental pollution. Among the actions individuals

can take to reduce their green-house gases associated with personal transportation, there is to operate their

current vehicles more efﬁciently. Behavioral theory strongly conﬁrms that the most important educational

element in changing driver behavior is the direct feedback while driving on an immediate and continuous

basis. Gamiﬁcation has been positioned as a powerful approach, tool, or set of techniques that guides targeted

behavior change and improves the way that various activities are undertaken so that those involved begin to

take the desired actions while they experience more fun, enjoyment, and pleasure in their tasks. Building on

this direction, we present conceptual approach of an eco-driving simulation system that aims to train drivers to

follow eco-driving rules simulating the augmented reality technology in virtual driving games. The proposed

system provides: i) an efﬁcient way to study the effect of AR games responsible for monitoring driving

behavior and delivering action personalized plans that will help user to maintain a green driving style without

distracting them from safe driving and ii) a multiplayer gaming environment where users can monitor the

eco-driving score evolution, set missions and invite other to participate collaboratively or competitively.

1 INTRODUCTION

A recent study (Alessandrini et al., 2012) shows that

road transport is responsible for about 30% on the to-

tal emissions of CO2 into the atmosphere. Among

the actions individuals can take to reduce their green-

house gases associated with personal transportation,

there is to operate their current vehicles more efﬁ-

ciently (Barkenbus, 2010). In certain situations, the

driver’s driving style can result in differences in terms

of fuel consumption (and therefore CO2 emissions)

from 2 up to 35% between a calm driver and an

aggressive one (Alessandrini et al., 2009)(Wengraf,

2012).It is crucial to educate drivers to adopt a dri-

ving style that is as eco-friendly as possible, in order

to reduce the environmental impact caused by road

transport. At this point, it should be also mentio-

ned that numerous studies have underlined the sub-

stantial ecological (Mensing et al., 2014), economic

but, also, road safety adverse beneﬁts that can be deri-

ved from adopting eco-driving behaviors (Barkenbus,

2010)(Mundke et al., 2006).

As expected, a plethora of people motivated to

further research the promotion of eco-driving. A way

to educate drivers to improve their driving skills is by

providing the necessary feedback about their driving

style in real time. Eco-driving training programs have

been implemented in numerous countries and they

have proven extremely efﬁcient from both ecological

and ﬁnancial aspect (Bari

c et al., 2013). But a cru-

cial problem still persists. Multiple countries actually

banned (Bell, 2013) the use of technological means,

like head-up-displays, during driving due to the fear

of driver’s distraction (Strayer et al., 2011). There is

strong evidence which indicates that the development

of a simulation system is fundamental in order to con-

ﬁrm and prove the safety of usage of such systems in

real life scenarios.

Our eco-driving simulation system aims to train

drivers in the use of Augmented Reality applicati-

ons during driving in a virtual environment with the

help of gamiﬁcation methods. Gamiﬁcation is an

umbrella term for the use of video game elements

to improve user experience and user engagement in

non-game services and applications (Deterding et al.,

2011). Studies show positive results from adoption of

gamiﬁcation (Hamari et al., 2014). The results of this

training method will be evaluated to answer scientiﬁc

questions about its usability, efﬁciency and potential

risks which the driver could face when driving a real

Gardelis, K., Lalos, A. and Moustakas, K.

Development of an Eco-Driving Simulation Training System with Natural and Haptic Interaction in Virtual Reality Environments.

DOI: 10.5220/0006619500940101

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 2: HUCAPP, pages

94-101

ISBN: 978-989-758-288-2

vehicle.

In fact, we refer to the design of two separate ap-

plications, a Virtual Reality one, which is a driving

simulator system, and an Augmented Reality one that

will be implemented in the non-real world of the ﬁrst

application, for research purposes. The implementa-

tion of the augmented reality application aims to im-

prove driving behavior (i.e. reducing fuel consump-

tion and atmospheric pollution by CO2 emissions).

Direct use of the Augmented Reality application

while driving a real vehicle, without proper study and

evaluation with real users, may cause even fatal acci-

dents. In the driving process, disorienting the driver’s

attention or reducing visibility by displaying digital

elements could be very dangerous. Poor interface and

interaction design of such an application is very li-

kely to lead to these results. To avoid such undesira-

ble consequences, and ensure the safety of drivers and

pedestrians, a simulation system is required.

The use of a driving simulator has many advan-

tages over similar real-world driving research, inclu-

ding experimental control, efﬁciency, expense, safety,

and ease of data collection (Alm and Nilsson, 1994)

(Yan et al., 2008). The simulator experiments are

possible to reproduce dangerous driving conditions

and situations in safety. Therefore, driving simulators

have a potential to analyze further certain events and

explore effective countermeasures without endange-

ring a human life.

In conclusion, with the use of such a simulator sy-

stem for training in eco-driving behaviors the follo-

wing beneﬁts are expected:

• Signiﬁcant reduction of fuel consumption and the-

refore CO2 emissions

• Decrease in accidents due to the embrace of a sa-

fer, less aggressive, driving style

This paper is structures as follows, in section 2 we

give an overview of the existing eco-driving assisting

systems, in section 3 we analyze the details of our

approach and ﬁnally in section 4 we end with discus-

sion, conclusion and future direction related to this

topic.

2 RELATED WORK

The promotion of eco-driving is separated in two ma-

jor categories. Eco-driving real-life applications and

simulators. There are numerous approaches already

available. The most representative are presented be-

low:

2.1 Real-Life Applications

Smartphone Applications

Studies have shown that eco-driving smartphone ap-

plications have impact on fuel efﬁciency (Tulusan

et al., 2012). Greenmeter (research & technology,

2008), FuelGood (Trust, 2016), TEXACARe (Texa,

2016) and Geco (Nouvelles, 2014) (Figure 1) are

good examples of smartphone applications that have

been developed in order to track fuel consumption and

increase efﬁciency. Some of them works in real time

since others provide a summary at the end of each

journey. TEXACARe and Geco provide a score ba-

sed on actions that have impact in fuel consumption

and driver’s behavior.

Figure 1: Screenshots of Geco - The eco driving guide.

(from ifp Energies nouvelles, 2014).

Remote Control Solution

WeNow (WeNow, 2015) is a solution mainly aimed at

vehicle ﬂeets and aids them in increasing their over-

all fuel efﬁciency. The device of the platform is con-

nected to the OBD II interface of the vehicle in order

to be able to collect the relevant vehicle data such as

mileage, fuel consumption, etc.

Advanced Driver-Assistance Systems

FIAT®eco:DRIVE APP (Fiat, 2014), Ford Smart Eco

Driving (Ford, 2013), BMW Eco Pro (BMW, 2012),

Honda®Insight Eco Assist system (Honda, 2017)(Fi-

gure 2), Nissan Eco-Drive Support Technology (Nis-

san, ), Subaru Ecology (Subaru, ) and Mitsubishi

ECO Drive Support (Mitsubishi, 2013) are fully im-

plemented systems. Some of them are based on apps

that communicate directly with the car which pur-

pose is to improve the driving style, lowering fuel

Development of an Eco-Driving Simulation Training System with Natural and Haptic Interaction in Virtual Reality Environments

consumption and tracking the carbon dioxide emissi-

ons. Others are build-in systems providing feedback

in real-time for maximizing the fuel efﬁciency. Fi-

nally, one approach acts as a medium between driver’s

actions and car’s engine inputs to avoid non-eco beha-

viors, although the system turns this functionality off

in cases of emergency.

Figure 2: Honda®Insight Eco Assist driving style efﬁciency

indication. (from Honda Motor Co., Ltd., 2010).

2.2 Simulators

The ST Software Simulator Systems Eco Driving

Package (Systems, 2007) (Figure 3) teaches how to

save up to 20 or 30% of fuel by applying an eco-

friendly style of driving. DriveSim simulator (Dri-

veSim, 2014), in the other hand, is a more realistic

simulator which includes real trafﬁc and pedestrians.

This program offers the possibility of doing different

tours with any climatic settings, timing and traction

(e.i. driving at dusk, on slippery surfaces, snowy en-

vironments etc.). In this simulator, eco-driving is just

an extra feature. Additional examples include an eco-

driving simulation system which also focus on slo-

wing the wear of car’s consumables (Seung Yoel Kim,

2016), and Mcllroy et al.’s (McIlroy et al., 2017)

experimental evaluation of an in-vehicle eco-driving

support system that provided auditory, visual, and vi-

brotactile stimuli.

Figure 3: Screenshot of ST Software Simulator Systems

Eco Driving Package. (from ST Software BV, 2007).

Games

EcoDriver (Eco-Drive, 2015) (Figure 4) is a mobile

game which tests the players driving in an endless,

randomly generated environment, in an attempt to de-

monstrate the advantages of driving safely, economi-

cally and in an environmentally friendly way to the

player.

Figure 4: Screenshots of EcoDriver. (from Google Play

Store, 2015).

3 SYSTEM DESCRIPTION

In this paper we propose conceptual approach of an

eco-driving simulation system that will combine all

the useful characteristics of the above-mentioned im-

plementations. The development of a realistic driving

simulator system which is focused on training drivers

to improve their driving behavior via real-time feed-

back presented through an Augmented Reality HUD.

All the crucial information will be presented within

their ﬁeld of view though a minimal design in a way

that they will not get distracted. In Table 1 are the

”golden rules” of eco-driving (as deﬁned in ECO-

WILL project (eco, 2010)) that we used as a staring

point of which information should be consider cru-

cial. Furthermore, a summary of their performance

will be available after the end of every session for self

evaluation and recapitulation. In the near future plan,

when the use of AR HUD while driving is permitted,

we can use the gamiﬁcation technique to encourage

more and more people to become eco-friendly drivers.

3.1 Architecture

As can be seen in Figures 8 and 9, the simulator is

a one-person simulator system including an adjusta-

ble seat mounted in a full motion platform, a steering

HUCAPP 2018 - International Conference on Human Computer Interaction Theory and Applications

Table 1: The ”Golden Rules” of eco-driving.

Rule Instruction

1. Shift up as soon as possible Shift up between 2.000 and 2.500 revolutions per minute.

2. Maintain a steady speed Use the highest gear possible and drive with low engine RPM.

3. Anticipate trafﬁc ﬂow Look ahead as far as possible and anticipate the surrounding trafﬁc.

4. Decelerate Smoothly

When you have to slow down or to stop, decelerate smoothly by

releasing the accelerator in time, leaving the car in gear.

Figure 5: Architecture Diagram.

wheel, a gear shifter, the clutch, gas and brake pedals.

The player is immersed in the simulated environment

by means of Oculus Rift and Leap Motion Sensor.

In the upper half, the real-world part, of Figure

5 we can see all the above-mentioned hardware and

how each one interacts with the driver and software

of the system. The driver is handling the steering

wheel, shifter and pedals. This steering input is sent

as a signal to the input management module of the

program. The input management module is com-

municating with the positioning management module

which is also getting input signals from Oculus Rift

and Leap Motion sensor with head and hands position

respectively. By having all the required data, positio-

ning management can now update the position and ro-

tation of all our active objects in the virtual world, the

driver’s virtual hands and body in order to get better

immersion experience through absolute position ma-

tching of real and virtual world’s objects. We also

update the virtual AR HUD and car’s position and

rotation so we can continue to the image generator

module where we render the image that will be pro-

jected in the Oculus Rift for the driver to get the vir-

tual environment perception. The positioning mana-

gement module is also communicating with the out-

put management module which is responsible for the

feedback that will be simulated to the driver through

the motion platform and the steering wheel. The mo-

tion platform is following the car movement accor-

ding with its speed and acceleration. On the other

hand, the steering wheel is simulating the forces that

are being applied to the car’s tires from the road sur-

face, the reinstatement of the wheel in its zero rotation

position and possible conﬂicts with hard objects like

walls, houses or other cars. Finally, we have the sound

management module which is receiving environmen-

tal ambient sounds and the car’s sound related to its

rpm, current gear and tires’ traction.

Development of an Eco-Driving Simulation Training System with Natural and Haptic Interaction in Virtual Reality Environments

3.2 Gameplay

The simulator system has multiple different tracks

with different difﬁculty levels based on track’s length,

inclination, sharp turns etc. For simplicity’s sake, the

gameplay will be explained using the easiest of the

tracks. Due to its small size, the track is divided in

only three segments (as seen in ﬁgure 6). The data

kept from driver’s performance will be independent

in each segment, by doing this the provided results

will be more accurate.

Figure 6: Track’s partitions pointed with red lines.

The trainee driver is assigned to complete at le-

ast two consequent laps. In the ﬁrst lap, the driver’s

performance will only be recorded in order to be ana-

lyzed and create a personalized driving proﬁle. From

the second lap and onward, the driver’s current per-

formance will be compared to the previous lap per-

formance in the same track’s segment providing real-

time feedback on how the eco-driving behavior could

be improved. Furthermore, to make eco-driving even

more appealing to the public, we tried to gamify the

training process by adding a ranking system. Every

trainee will be ranked according with the evaluation

of his performance, enabling him to compete with the

other drivers alone or as a team by inviting his friends.

The real-time feedback will be provided via the

AR HUD simulated system enabling the driver to self-

evaluate instantly without being distracted due to the

minimal layout that is being used. The presented eco-

driving related data selected based on numerous stu-

dies such as (Andrieu and Saint Pierre, 2012) and are

the following:

• Average Revolutions Per Minute (RPM) during

gear ups

• Average RPM during gear downs

• Percentage of time when there is no input given

to the car (throttle or brake) over the total driving

time. (Engine Break)

• Cruising speed while having the highest gear

engaged and relatively stable speed.

• Number of recorded sudden breaks

• Average car acceleration.

Except the eco-driving related data, there is also

presented:

• Vehicle’s current speed

• Vehicle’s current RPM

• Vehicle’s current gear

• Ranking table

As seen in Figure 7, the above-mentioned data

seems to be projected at the car’s windshield. The

left image (a) shows the AR glasses that are virtually

mounted in the driver’s head. Through each lens is

slightly visible the AR viewport for each eye. The

right image (b) shows all the data that are being pre-

sented to the driver every moment. As referred pre-

viously, the data layout is designed in a way to not

block the road visibility.

3.3 Prototype

Unity Game Engine used for the virtual world imple-

mentation of the prototype. For hardware, we used

a steering wheel with shifter and pedal which were

mounted on a motion platform. Then, using the spe-

cial mount, we placed the Leap Motion sensor in the

center of Oculus Rift to accurately detect the driver’s

hands. Finally, using a tripod, the Oculus Sensor was

placed in front of the drive platform. A viable alter-

native tested was the use of 3D projector instead of

Oculus Rift.

4 DISCUSSION & CONCLUSION

To conclude, our conceptual approach indicates posi-

tive results of the use of eco-driving simulation trai-

ning systems in order to test projects which is still un-

der development and its consequences are uncertain

in real world cases. Though it is a beneﬁcial solu-

tion, the use of such simulation systems is not wit-

hout challenges and limitations, most notably that of

realism gap due to the vast number of parameters that

needs to be implemented. But there are still obstacles

to overcome, even if the simulation system was ﬂaw-

less there are still lots of steps to be taken before the

use of AR driver-assistance systems in real life, such

HUCAPP 2018 - International Conference on Human Computer Interaction Theory and Applications

(a) (b)

Figure 7: (a)Viewport window of the AR glasses (b) All the AR data that are being projected in the windshield.

Figure 8: Leap motion sensor placed on Oculus Rift.

Figure 9: Right side view of the prototype.

as ethical issues like the ban of using technological

means while driving. Additionally, AR HUD are not

yet fully capable of coping tasks like that, the limited

ﬁeld of view and the low refresh rate are two examples

of the functionalities need to be improved as soon as

possible. Ultimately, the AR technology has a limited

target group, people that have a driver’s and they are

in early adulthood since it is challenging for someone

older to get used to cutting-edge technologies. On the

other hand, the driving simulation system is a favora-

ble way to teach teenagers how to drive using the AR

drive-assistance system from the very beginning.

4.1 Future Direction

A basic attribute of good simulator systems is not only

the simulated model of the vehicle but also the sur-

rounding environment in which the model is moving.

The key element is great detail and realism. With the

addition of more parameters the system will be a bet-

ter approximation of real-world cosmos that will lead

us in a realistic fuel consumption metric, by using the

Orﬁla et al. (Orﬁla et al., 2017) fuel consumption mo-

del. Moreover, the implementation of an Artiﬁcial In-

telligence System is fundamental to control the other

drivers’ behaviors based on real drivers’ reactions in

unexpected events. The system will also control pede-

strians accordingly. Therefore, we will be able to cre-

ate controlled scenarios in order to study the driver’s

behavior. For example, a virtual child could suddenly

appear in the middle of the road to check the driver’s

reaction. A comparative study can be carried out bet-

ween driving using the augmented reality system and

without using it. By gathering quantitative and quali-

tative data through such studies, we can expect results

on how much the use of the augmented reality system

affects the reaction time of each driver. In conclusion,

the purpose of our studies is to result in solutions that

will ensure the safety of pedestrians and drivers while

using the AR system. For example, the use of a war-

ning indication in the augmented reality system could

alert the driver of an upcoming unexpected danger.

These indications should be designed and evaluated,

with corresponding experiments.

The AR driver-assistance systems are still an un-

charted area, with results that do not yet have imple-

mentations in commercial level. In our days, the only

limitation on applications that can be developed in

this ﬁeld is the limits of human imagination. Bearing

this in mind, high priority in our list of future exten-

sions has the modiﬁcation of the present system into

Development of an Eco-Driving Simulation Training System with Natural and Haptic Interaction in Virtual Reality Environments

(a) (b)

Figure 10: GameCar Mobile Application Mockups (a) Main Menu (b) Boosts (c) Race Preparation (d) DrivingLog.

a platform where any developer will be able to deve-

lop and test such applications in our simulator before

testing it real-world conditions. By building a driving

simulator to be used as a testing base for AR appli-

cations, the developing time will be improved due to

the decrease of evaluation and testing time which will

lead in higher quality results.

Finally, we are already working on making more

appealing the whole idea of eco-driving to the public

by gamifying the training process. By using an OBD

II Scanner the drivers will be evaluated while driving

and through a rewarding system they would be able

to redeem their rewards in a driving management si-

mulation game that will be available all platforms. In

Figure 10 are presented the game’s mockups. The dri-

ver will collect coins, points and boosts according to

his real-world driving sessions with which he will im-

prove his in-game driver skills and car parts. Impro-

vement of statistics will help him to climb the ranking

ladder and emerge as an example in the eco-friendly

driving community.

4.2 Conclusion

Current self-management systems related to eco-

driving, despite their complexity and sophisticated

nature, are practically proven inefﬁcient, since they

do not take into account a mixture of complex cha-

racteristics including age, sex, social status, ﬁnancial

status, physiological measurements, driving behavior

measurements and vehicle characteristics. The pro-

posed simulator is designed to ﬁll this commitment

gap by introducing an innovative and interactive Seri-

ous game that guide targeted Driving behavior chan-

ges to improve the way that various eco-driving acti-

vities are undertaken so that the drivers involved will

be educated to take the desired actions and more im-

portantly will be familiar with AR displays and appli-

cations while driving.

ACKNOWLEDGMENTS

This work has been supported by the H2020-ICT-24-

2016 IA project GamECAR (Grant No. 732068).

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