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
94
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
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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
95
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
96
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
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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
98
(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
99
(a) (b)
(c) (d)
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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