Evaluation of Driving Efficiency and Safety with a Custom-Developed

Simulation Scenario

David González-Ortega, Francisco J. Díaz-Pernas, Mario Martínez-Zarzuela

and Míriam Antón-Rodríguez

Department of Signal Theory and Communications and Telematics Engineering, Telecommunications Engineering School,

University of Valladolid, Valladolid, Spain

Keywords: Driving Simulation, Unity, Modeled ICE Vehicle, Driving Learning, Driving Efficiency, Driving Safety.

Abstract: In this paper, we present a custom-developed driving simulation scenario and a vehicle model developed to

evaluate driving efficiency and safety. The scenario includes different road sections, traffic conditions, and

events, including a through road similar to a real road section in the city of Valladolid (Spain). The modeled

vehicle is an ICE vehicle with manual or automatic gear shift. During a simulation, following a guided or a

free route, the speed, rpm, gear, consumption, and traffic offences are showed in real time and stored in files

for further processing. Six people drove in the scenario twice, one time with automatic gear shift and

another with manual gear shift. An analysis of the results has been carried out to know the factors with

influence on driving efficiency and safety. A significant relation between efficient and safe driving was

found. People that took part in the experiments ranked the simulation scenario positively regarding ease of

interaction, realistic experience, usefulness for driving learning, and entertainment capacity.

1 INTRODUCTION

Driving safety and efficiency are major topics

worldwide with the growing number of vehicles and

the complexity and variety of road networks.

Although the rate of deaths relative to the world’s

population has stabilized and even declined

considering the number of motor vehicles in the past

years (World Health Organization, 2018), their

figures are very high. They cause unacceptable

human and economic costs in all the countries

regardless their level of development. Particularly,

road traffic injury is the leading cause of death for

young adults aged until 29 years old. From these

young adults, novice or drivers with low experience

are a significant part. The increasing pollution

levels, especially in urban areas, and the ongoing

exhaustion of fossil fuels require to reduce their

consumption. The use of efficient driving techniques

and the progressive substitution of ICE (Internal

Combustion Engine) vehicles for electric vehicles

are necessary to address these problems. In short, the

aim of achieving a safe and sustainable transport

demands drivers with highly developed safe and

efficient driving skills.

With the continuous progress in technology,

simulation environments have increased their

realism and learning potential. Thus, simulators can

speed up the process of acquisition of skills

necessary in many fields. Driving simulators make it

possible the acquirement, development, and

measurement of driving skills without the risks

associated with real driving using varied roads and

events where safety and efficiency can be quantified.

Driving simulators have been greatly used in

training programs in different countries. For

instance, in USA several projects based on driving

simulators have been developed (Allen et al., 2007).

In Brazil, simulation driving is included in the

official learning to obtain a driving license.

Moreover, driving simulators have also been used in

many research studies in different fields such as

engineering, psychology, and medicine (Zhao et al.,

2018). Driving simulation research has a series of

advantages compared to research based on real

driving. One of the most important advantages is the

capability to create virtual environments with fully

controllable parameters that would be, at the very

least, challenging and expensive in real driving

(Olstam et al., 2008).

In the presented work, a driving simulation

González-Ortega, D., Díaz-Pernas, F., Martínez-Zarzuela, M. and Antón-Rodríguez, M.

Evaluation of Driving Efﬁciency and Safety with a Custom-Developed Simulation Scenario.

DOI: 10.5220/0007930903010308

In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2019), pages 301-308

ISBN: 978-989-758-381-0

301

scenario with different road sections and traffic

events and a vehicle model to analyze the driving

efficiency and safety are presented. Both the

scenario and the vehicle model have been developed

with the Unity game engine (Unity, 2019). Unity is

one of the most used game engines in limited budget

projects and allows to develop games and graphical

applications. Besides, it is possible to export the

applications to 27 different platforms including PC,

mobile, and console ones. An important feature of

the last version of Unity is its integration with virtual

reality platforms such as Oculus Rift.

Our driving simulator shows in real time and

stores in files for further processing, not only

information about the user’s vehicle (position,

speed, rpm (revolutions per minute), gear, and fuel

consumption), but also the traffic offences

committed during simulation. This information can

be used to analyze the different simulations fulfilled

by the drivers so that conclusions can be drawn

about their driving competence.

The rest of the paper is organized as follows.

Section 2 presents the state of the art on driving

simulators. Afterwards, Section 3 explains the

developed driving simulation scenario and user’s

vehicle. Section 4 details the obtained experimental

results and, finally, Section 5 draws the main

conclusions about the presented simulator.

2 DRIVING SIMULATORS

A simulator is a hardware and software

configuration in which, through algorithms, the

behavior of a process or physical system is

replicated. In the education field, simulators are

means to form concepts and to build knowledge and

also means for their application to new contexts that

people, for several reasons, cannot have access to

from the methodological context where their

learning is developed. Simulators have educational

advantages such as the providing of open learning

environments based on real models and that users

adopt an active role, turning themselves into the

builders of learning from their own experience. To

name some examples, simulation has been applied to

fields so different as nuclear power engineering (Cui

et al., 2017) and pediatric emergency medicine

education (Sagalowsky et al., 2016) with satisfactory

results.

All the above mentioned is clearly applicable to

driving simulators. Although there are driving

simulators aimed at the pupils learning to drive

(Simumak, 2019) from which they can learn to drive

from scratch, most of these simulators aim to

entertain leaving aside the development of

competences towards responsible driving.

(Stinchcombe et al., 2017) found a statistically

significant association between video game

experience and risk-taking behaviors (large values of

speed and crashes) when the participants had to

drive in a scenario developed specifically to assess a

particular skill such as handling. As a consequence,

simulators for learning and assessing safe and

efficient driving should be carefully designed to

reinforce proper driving behaviors through the

provision of feedback.

In the last years, driving simulators have been

used in many research studies for a wide variety of

purposes. With them, drivers’ reactions to different

situations, which are not feasibly replicated on real

roads, can be analyzed. Their suitability to assess the

behavior of drivers and as a means to learn safe and

efficient driving skills has been shown. (Lee et al.,

2018) demonstrated that a driving simulator

methodology including instructions, realistic traffic

scenarios, and adaptive analytical methods is

suitable to study drivers’ behavior and their

interactions with road users. (Yuan et al., 2019)

studied the safety effects of weaving length, traffic

condition, and drivers’ characteristics in mandatory

lane change behavior using a simulator. (Almeida et

al., 2014) presented a simulation scenario based on

the Serious Game concept to develop way-finding

behaviors in emergency situations. (Meng at al.,

2019) studied the relation among drivers’

characteristics, fatigue, and performance in an

experiment with 50 taxi drivers. (Bıçaksız et al,

2019) investigated the relation of functional and

dysfunctional impulsivity with driving style by

measuring driver behaviors on a driving simulator.

All the research studies mentioned above obtained

interesting findings and meaningful results. Focused

on driving efficiency, (Zhao et al., 2015) developed

an eco-driving support system based on a driving

simulator that was validated as a useful tool to save

fuel consumption and reduce emissions. (Jamson et

al., 2015) studied with a simulator the tradeoff

between driving efficiency and safety using systems

embedded in the vehicle to advise the driver about

the use of the throttle pedal. They showed that

efficiency can be improved using these systems.

(Pampel et al., 2015) carried out a simulator study

showing that drivers have mental models of eco-

driving that they do not use when instructed to drive

normally.

In driving simulators, scenarios to analyze the

level of driving efficiency and safety, the relation

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302

between them, and the results produced as a function

of them can be included. When people see

themselves in a real driving environment such as a

city with heavy traffic or a complex junction, they

may think that they will be unable to face that

challenge. In particular, driving anxiety is common

in older adult drivers, which can contribute to

decisions to limit or avoid driving (Taylor et al.,

2018). Driving simulators enable users to face many

of these problems in a virtual environment without

endangering the user or other drivers and to obtain

driving skills to overcome these problems, in such a

way that these skills can be applied when they face

similar situations in real driving. The users can also

get used to the vehicle controls through simulations,

manipulating them with the steering wheel, pedals,

and gear lever similarly to in a real vehicle. For that

purpose, different types of peripherals can be used,

from controls to complete driving cabin, including

devices such as Logitech G27 or Fanatec CSL Elite

Wheel Advanced Pack. Obviously, simulators

enable users to repeat simulations in a scenario and

to deal with certain events many times. Besides, it is

possible to simulate real situations that would be

practically impossible to do voluntarily in a real

environment. Situations such as a vehicle stopped in

the middle of a road or a pedestrian crossing a road

unexpectedly are events that can be replicated in a

simulator easily. This will enable simulator users to

be better prepared for unusual situations that may

find in the future. Simulators can also be utilized to

make studies about how certain agents, such as

fatigue (Meng et al., 2019), physical or

psychological state of the driver, medicine, drugs

(Bergeron et al., 2014), alcohol consumption (Van

Dyke et al., 2014), or age (Freydier et al., 2014;

Ledger et al., 2019), can influence driving capacity

significantly.

Lastly, it is important to mention that simulators

can gather data in a simple way. Thus, users can

have access to information of the fulfilled

simulations and observe their evolution. This

enables drivers to see and be aware of their mistakes

and committed traffic offences and what aspects

they should improve to avoid them in the future.

3 CUSTOM-DEVELOPED

DRIVING SIMULATION

SCENARIO

We have developed a driving simulation scenario of

7.7 km covering different types of roads and traffic

elements and events that drivers should address

adequately. The Unity game engine has been used to

create the scenario. Unity was adopted in many

serious game projects (Unity, 2019; Rodrigues et al.,

2018; Almeida et al., 2014). It is compatible with a

great range of 3D design tools such as Blender and

3ds Max and allows to develop 2D and 3D games. A

part of the scenario is similar to a section of a

through road in the city of Valladolid (Spain). This

section has been extended with other sections to

complete the scenario. The upper section is an

interurban road with one lane per direction and the

lower section is an interurban road with two lanes

per direction (partly in slope).

The user’s vehicle has been modeled with the

Unity module Realistic Car Controller, which can be

adapted to achieve the features and behaviour of the

desired vehicle. This vehicle is a Volkswagen Passat

Highline 2.5 V6 from 2001. We chose a sedan

vehicle close to the average age of vehicles in Spain.

We have adapted the parameters and GameObjects

of the Realistic Car Controller module, such as rigid

body, mesh collider, wheel collider, cameras, lights,

and box trigger. To model the fuel consumption, we

have determined graphs setting the dependency of

the speed and the current gear on the fuel

consumption. The vehicle can be driven not only

with manual gear shift but also with automatic gear

shift. It must be noted that automatic vehicles will be

much more common in the future as all electric

vehicles are automatic. The driver can also select

front-wheel drive, rear-wheel drive, or all-wheel

drive before the simulation.

To include the rest of the vehicles in the

scenario, we have used the Unity module Easy

Traffic. 1460 waypoints have been distributed in the

scenario and different vehicles such as pickup

trucks, sedan and off-road vehicles, and motorcycles

move along different waypoints and with different

speeds. The density of vehicles can also be

configured. Pedestrians are included in the scenario,

which walk on the sidewalk, can cross the pedestrian

crossings, wait for a green traffic light, or even cross

a road unexpectedly to study the response of the

driver.

The peripheral input device used in the simulator

is the Logitech G27, which includes steering wheel,

gear lever, and clutch, brake, and throttle pedals.

The levers and buttons of the Logitech G27 have

been configured to associate them with actions such

as start the engine or switch on or off the lights or

the turn signals.

A system to control the traffic offences has been

added in the scenario. The most important computed

Evaluation of Driving Efﬁciency and Safety with a Custom-Developed Simulation Scenario

303

traffic offences are: exceed the speed limit, drive

under the minimum speed, failure to stop at a stop

sign, failure to stop at a traffic light, failure to yield

at a yield sign, not switch on the corresponding turn

light in a turn, in an overtaking, or entering or

leaving a road, be closer than the minimum distance

to the vehicle ahead, drive off the road, wrong-way

driving, not letting a pedestrian cross a pedestrian

crossing, striking a pedestrian, collision with other

vehicle, and collision with street furniture. When an

offence is committed, its type together with the time

and the position of the user’s vehicle in the scenario

are recorded. Besides, a textual message is shown in

the scene to inform the driver about the offence if

this configurable option is selected before the

simulation. Apart from the offences, the position,

speed, rpm, selected gear, and fuel consumption are

recorded with a configurable sampling rate. After

the simulation, data is stored grouped as a function

of the driver, easing the access and comparative

study of the results.

Figure 1: Simulation scene with the camera inside the

vehicle.

Figure 2: Simulation scene with the camera outside the

vehicle.

Before the simulation, the user can choose to

have a guided or free route. There is a Unity camera

inside the vehicle so that the driver can have a field

of view similar to real driving as shown in Fig. 1. On

the top left corner of the scene, there is a

speedometer and on the top right corner there is an

rpm meter and a number with the selected gear. The

user can turn their view left or right by pressing

particular buttons on the steering wheel. On the

bottom right corner of the scene, there is a map,

similar to a map shown by GPS navigation devices,

with the location of the vehicle in the scenario,

which is updated in each frame. An image of the

user while driving is added on the right part of the

scene shown in Fig. 1. While driving, the user can

change from the view present in Fig. 1 to a view

outside the vehicle, as shown in Fig. 2, which can be

interesting for learning purposes. Fig. 3 shows a

scene with the camera inside the vehicle after

turning the view left to see the left mirror. Fig. 4

shows a scene with a textual message on the right

informing about the recently committed offence of

not letting a pedestrian cross a pedestrian crossing.

Figure 3: Simulation scene with the camera inside the

vehicle.

Figure 4: Simulation scene with the camera outside the

vehicle.

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4 EXPERIMENTAL RESULTS

Six users drove in the simulation scenario along the

same guided route. They drove in the scenario twice,

firstly with automatic gear shift and secondly with

manual gear shift. Table 1 shows the characteristics

of the users that we asked them about to study their

influence on the driving performance. Tables 2 and 3

show the data obtained with the automatic and

manual gear shift, respectively. The percentage of

time in 1st gear also includes the time that the

engine was idle due to a red traffic light or for other

reasons such as letting pedestrians cross a pedestrian

crossing. From these tables, a clear variation of the

fuel consumption among the users can be observed

as a function of their driving profiles. Conversely,

the differences between the automatic gear shift and

the manual gear shift are generally small for each

driver. All users but user #6 have consumed more

with manual gear shift, with an average fuel

consumption increase of 0.4 l/100 km with manual

gear shift. This was partly caused by the longer time

in 1st gear than in 2nd gear with manual gear shift,

unlike with automatic gear shift. Users #2 and #6

have achieved a lower fuel consumption with

manual gear shift by using longer gears (4th and 5th

gears) more time than the rest of the users, which

have used shorter gears more time than required in

efficient driving. Another related factor influencing

the higher driving efficiency for users #2 and #6 was

the higher average speed they have achieved.

Tables 4 and 5 show the offences the users have

committed with the automatic gear shift and manual

gear shift, respectively. The speeding offence was

the most frequent as the simulator strictly records

the speeds larger than the speed limit of 50 km/h in

the through road and of 90 km/h in the two

interurban sections. Although the user #2 had a

larger average speed both with automatic and

manual gear shift, he or she was not the driver with

the largest number of speeding offences. The users

with the lowest number of offences were the #3 and

#6, both with 8 offences. The user #6 was the most

efficient driver with manual gear shift and one of the

most efficient with manual gear shift. The user #3

was one of the most efficient drivers with both

gears. Conversely, the user #1 was the driver that

committed the most number of traffic offences with

both automatic and manual gear shift and was also

the driver with the highest average fuel consumption

with both automatic and manual gear shift. Thus

there was a relation between driving efficiency and

safety as users that drove more efficiently also drove

safer and vice versa.

Regarding the overall number of offences, there

was a decrease of 36% with automatic gear shift,

partly because the drivers did not have to pay

attention to the clutch and gear lever to shift among

gears. There has not been found a relation between

some users’ characteristics (age, driving experience,

videogame experience, and annual mileage) and the

level of driving efficiency and safety as the two

users with the most efficient driving (#2 and #6)

have different characteristics’ profiles, like the two

users with the safest driving (#3 and #6). Similarly,

the users with the least efficient driving (#1 and #4)

and the users with the least safe driving (#1 and #5)

have different characteristics’ profiles. From these

results, it must be noted, on the one hand, that

driving profiles are complex and difficult to be

related to the drivers’ characteristics and, on the

other hand, that a high videogame experience has

not had influence on a better performance in the

simulation scenario, which supports the use of the

scenario in driving research studies.

After using the simulator, the six users were

asked about the more relevant aspects of its use.

Table 6 shows the results of the users’ feedback. All

the aspects were rated positively above 7 out of 10

and the overall rating was 7.7. The simulator was

considered a useful tool for driving training due to

its ease of interaction and similarity to real driving.

Moreover, the simulator kept them entertained.

Table 1: Characteristics of the six users that took part in the experiments in the simulation scenario.

User Age Driving experience (years) Videogame experience Annual mileage(km)

#1 33 15 Low 14,000

#2 24 6 High 6,000

#3 25 7 Moderate 10,000

#4 26 8 Low 30,000

#5 26 8 High 1,000

#6 44 16 Low 12,000

Evaluation of Driving Efﬁciency and Safety with a Custom-Developed Simulation Scenario

305

Table 2: Data of the routes in the simulation scenario with automatic gear shift.

User #1 #2 #3 #4 #5 #6 Average

Time 12´16´´ 10´33´´ 12´01´´ 11´52´´ 11´11´´ 11´54´´ 11´37´´

Average speed (km/h) 47.7 57.9 47.9 46.7 48.8 46.7 49.2

Average fuel consumption (l/100km) 8.2 6.5 7.1 8.0 7.6 7.4 7.46

% of time in 1st gea

26.1 27.5 27.5 26.7 20.2 24.1 25.3

% of time in 2nd gea

29.7 31.3 27.3 36.3 33.1 33.6 31.8

% of time in 3rd gea

28 18.6 29.5 16.9 33.1 27.3 28.0

% of time in 4th gea

9.7 15 15.7 20.1 13.6 6.3 13.3

% of time in 5th gea

6.5 7.6 0 0 0 8.7 3.7

Table 3: Data of the routes in the simulation scenario with manual gear shift.

User #1 #2 #3 #4 #5 #6 Average

Time 12´02´´ 9´33´´ 11´53´´ 13´15´´ 12´13´´ 11´41´´ 11´46´´

Average speed (km/h) 49.4 57.5 47.7 46 46.4 49.3 49.3

Average fuel

consumption (l/100km)

8.8 6.9 7.5 8.3 7.7 6.2 7.5

% of time in 1st gea

34.3 15.4 30.5 30.6 28.2 24.3 27.2

% of time in 2nd gea

11.6 24.5 24.4 29.1 24.1 14.9 21.4

% of time in 3rd gea

37.7 27.1 13.6 35.1 28.2 25.8 27.9

% of time in 4th gea

16.4 26 31.6 5.2 11.9 35 21

% of time in 5th gea

0 7 0 0 7.6 0 2.4

Table 4: Traffic offences in the simulation scenario with automatic gear shift.

User #1 #2 #3 #4 #5 #6 Total

Exceed the speed limit 3 3 1 1 4 1 13

Drive off the road 2 0 1 4 1 1 9

Failure to stop at a traffic ligh

0 0 0 0 1 0 1

Collision with other vehicle 1 0 0 0 0 0 1

Collision with st

eet furniture 0 0 0 0 0 0 0

Not switch on the turn light in a turn 1 0 1 0 0 0 2

Total 7 3 3 5 6 2 26

Table 5: Traffic offences in the simulation scenario with manual gear shift.

User #1 #2 #3 #4 #5 #6 Total

Exceed the speed limit 5 4 3 1 4 2 19

Drive off the road 2 0 1 3 2 3 11

Failure to stop at a traffic ligh

1 1 0 0 0 1 3

Collision with other vehicle 1 2 0 0 0 0 3

Collision with street furniture 0 0 0 0 1 0 1

Not switch on the turn light in a turn 1 2 1 0 0 0 4

Total 10 9 5 4 7 6 41

Table 6: Opinion of the users about driving in the simulation scenario.

User #1 #2 #3 #4 #5 #6 Total

Ease of interaction (0-10) 6 8 7 8 7 7 7.2

Similarity to real driving (0-10) 8 8 6 7 7 8 7.4

Usefulness for driving learning (0-10) 8 7 8 8 8 8 7.9

Entertainment (0-10) 9 10 7 8 7 8 8.2

Total 7.75 8.25 7 7.75 7.25 7.75 7.7

5 CONCLUSIONS

In this paper, a custom-developed simulation

scenario, which was created with the Unity game

engine, has been presented. The scenario has

different road sections, traffic conditions, and

events. It can be used both for the learning of

driving skills and the study of driving efficiency

and safety. One section of the scenario is similar to

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a real road section in the city of Valladolid (Spain).

Six people drove in the scenario twice, one time

with automatic gear shift and another with manual

gear shift. An analysis of the results has been

carried out to know the factors that have a strong

influence on driving efficiency and safety. Users

that drove more efficiently also drove safer and

vice versa. The relation between driving efficiency

and safety states the importance of developing in

parallel efficient and safe driving skills to achieve

a better road traffic. People that took part in the

experiments driving in the simulation scenario

highlighted its ease of interaction, realistic

experience, and usefulness for driving learning.

Future data stored in the scenario simulations from

many users will allow to carry out deep

comparative analysis to associate the different

drivers and their driving styles with their safety

and efficiency level, and to assess the evolution in

the development of driving competence.

ACKNOWLEDGEMENTS

This work was partially supported by the National

Department of Traffic (DGT) of the Ministry of the

Interior (Spain) under research project SPIP2017-

02257 and the MOVILIDAD INVESTIGADORES

UVa-BANCO SANTANDER 2019 Scheme.

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SIMULTECH 2019 - 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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