Performance Enhancement of Formula One Drivers with the Use of

Group Driven Learning

A. A. Moghaddar

1 a

, F. A. Bukhsh

and G. W. J. Bruinsma

Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands

Faculty of Behavioural, Management, and Social Sciences, University of Twente, Enschede, The Netherlands

Keywords: Process Mining, Machine Learning, Linear Regression, Formula One, Driver Performance, Racing,

Simulator, Group Driven Learning.

Abstract: Within motorsports less experienced drivers lack pace and performance compared to their peers. Training

these drivers requires time, which, due to the regulations and resources, teams often do not have. Less

experienced drivers are expected to perform at the same level as experienced drivers. This paper has the aim

of analyzing the abilities and performances of both drivers within a Formula One team to redesign the driver

training method. The focus is to provide drivers with real-time insights and feedback on their performance

during a simulator training session. By using a combination of the principles of process mining and statistical

analysis, data markers are created on the track. Based on the differences in telemetry, visual feedback is

provided to the driver. Throughout the research, this manner of training has proven to be promising. Drivers

showed an increase in their overall performance and an increase in car control and confidence. Despite these

promising results more experiments need to be done to guarantee a consistent outcome and to prove the

effectiveness of this training program. To continue developments, further research can be conducted on the

topic of visualization and communication.

1 INTRODUCTION

The current paper poses a novel method for drivers to

improve their performance using a team-based

learning approach that is applied within a race

simulation environment. According to de Winter, van

Leeuwen, and Happee (2012), driving simulators

offer various advantages, compared to the

implementation of the training within the real

environment. As de Winter et al. (2012) mentioned,

the first, and most important advantage of using

simulators is the possibility of encountering

dangerous driving conditions without being

physically at risk. This offers the learning driver to

explore the positive or negative consequences of

actions without leaving the driver vulnerable to

potential harm (Slob, 2008). Secondly, the

controllability of conditions, the reproducibility of

scenarios, and the standardization of ground rules are

built upon tests for the next line of advantages of

using driving and motion simulators. Combining

https://orcid.org/0000-0001-5621-4583

https://orcid.org/0000-0001-5978-2754

https://orcid.org/0000-0001-9365-9821

these parameters in a dynamic scenario provides

opportunities for controlling potential real-life

scenarios that may happen during a race (Wassink et

al. 2006). Adjusting the parameters of the virtual

scenario can, according to Wassink et al (2006),

enhance the reaction of the learning driver by

standardizing procedures, aiming at minimizing the

impact of the change within the environment. These

changes can differ per configuration. In research

conducted by Slob (2008), the effects and differences

in the various configurations are discussed

concerning their degree of freedom (DoF), the visual

element, and the feedback element.

Within this background research, the conclusion

defined a set of criteria that need to be taken into

consideration when building the simulator. Within

chapter 3, these differences and effects of each

configuration are discussed. Thirdly, de Winter et al.

(2012) described the accuracy and ease of data

collection as another advantage, contributing to the

reliability of the provided feedback, and offering

260

Moghaddar, A., Bukhsh, F. and Bruinsma, G.

Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning.

DOI: 10.5220/0011274400003274

In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022), pages 260-270

ISBN: 978-989-758-578-4; ISSN: 2184-2841

better opportunities for providing better feedback and

instructions. Based on all the aforementioned

advantages, Slob (2008) mentioned one other

advantage, describing the potential reduction of costs

compared to the alternative (real) training solution.

1.1 Feedback

Within the context of driver training receiving proper

and relevant feedback on performance contributes to

the abilities and overall performance of the driver.

The customization of feedback according to the needs

of the driver amplifies the strengths of this specific

driver within certain situations. According to Feng

and Donmez (2013), driver characteristics are good

predictors of the type and severity of exhibited risky

driving behavior when constructing systems to give

proper corrective feedback. Not only are the driver

characteristics important when constructing

personalized feedback, but taking into account the

acceptance and the preferred type of feedback plays

an important role. The visualization and presentation

of the corresponding feedback determine whether or

not the driver is going to open up to accept and

embrace the feedback (Anseel, & Lievens, 2009).

While it is recommended to use acoustic

feedback only to provide basic feedback to prevent

distracted driving, visual feedback can be used in

many forms to simplify the data as much as possible

while remaining the message clear and

understandable. According to two independent

studies on the detailed effect of visual feedback

conducted by Adams, Gopher, and Lintern (1977) and

Hoppe, Sadakata, and Desain (2006), visual feedback

contributes to the general development of motor

learning, leading to a better understanding of the

situation and hence increasing the likelihood of

interpreting the circumstance faster as well as with

more reliability. When kept simple and

understandable, providing visual feedback on a

driver’s performance can effectively improve the

driver’s learning curve. A better understanding of the

situation can be ensured due to interactivity and hence

the judgment in handling situations is refined for the

better.

1.2 Feedback as a Base for Social

Learning

Social learning is the general term of training based

upon data from various drivers while aiming to keep

the contents of the training personalized for the

current driver. Within this research, social learning

and team-based learning are applied to remove

redundancies, to improve the quality of the overall set

of data, wherein quality standards are defined by the

amount of “high” classified data sets as defined in

section 4.2, and to inform both drivers about the

habits of the other driver to show them an alternative

route through the processes.

1.3 Research Questions

The research questions addressed in this paper

therefore are:

• how can we harvest and preprocess data from

a race simulation environment to retrieve

individual performance metrics.

• how can we assess individual and team

performance

• How can we provide visual feedback to the

driver for performance improvement

purposes?

2 METHODOLOGY

Exploiting the training of athletes within this level of

expertise requires accurate data and reliable

background information. Without a proper

background of what the athlete requires, the training

might miss the major point of improvement and hence

lead to poor, unexpected results. This paper is

inspired by the CRISP-DM research methodology as

introduced and defined by Azevedo and Santos

(2008). CRISP-DM is a development approach used

as a framework for data-related research. Within this

section, the different possibilities for mimicking the

environment, data harvesting, data analysis, and

visualization are explored to create the backbone of

this project and therefore the backbone of the training.

2.1 Simulator Configuration

The experiments require an environment that

attempts to mimic the real environment of the drivers

within a race. For this purpose, a racing simulator is

built to recreate the direct environment. The simulator

configuration consists of a combination of the

following hardware elements:

• Wheel Base: Podium Wheel Base DD2 -

Direct Driven

• Steering Wheel: Clubsport Steering Wheel

Formula V2.5 X + Quick Release

• Pedal set: Clubsport Pedals V3 Inverted

• Damper: Clubsport Pedals V3 Hydraulic

Damper Kit + Brake Performance kit

• Cockpit: RennSport Cockpit V2

• Seat: Sparco Pro 2000 QRT Seat for

RennSport Cockpit

• Visual: Triple Monitor setup

Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning

261

The software environment used for the simulation

is the F1 2020 game developed by Codemasters. The

game is publicly available on Steam and offers

various configurations for data transmission over

UDP.

Figure 1: The Simulator Setup.

2.2 Data Preparations

Following the CRISP-DM Methodology, the next

step is the collection and preparation of data. The

Codemasters F1 2020 game is supported by an API

that provides the possibilities for extracting game data

from a racing session. This list of data that can be

obtained is structured in a set of packets that each

correlate to one section of the total dataset available.

Within the F1 2020 game, it is possible to limit the

frequency of updates to 10Hz, 20Hz, 40Hz, and

60Hz. The data packets, however, do not come in

simultaneously and therefore data might be

overwritten. Hence the upload rate of the database

must be higher. For the matter of limiting the amount

of data and reducing the overall redundancy of the

data, the frequency of updates is set to 10[Hz] and a

threshold on the relevancy parameter of the data

packet is introduced. Whenever the relevance of the

data packet is below 50% and the data packet does not

contain crucial data, this packet is ignored in the UDP

queue, and hence is not sent to, nor received by, the

application. Therefore, the packets that are sent and

received are the following:

• Header Packet

• Session Packet

• Lap Data Packet

• Car Telemetry Packet

• Car Status Packet

In essence, the crucial factors that are required for the

implementation of this training method include the

orientation of the car (Car Status Data), the current

track distance (Lap Data), the current lap time (Lap

Data), the best lap/sector times [Session Data] and the

telemetry (Car Telemetry). These elements can be

traced back to the various packets mentioned above.

3 SYSTEM DESIGN

The CRISP-DM methodology includes three steps as

part of the processing of data: Data Understanding,

Data Preparation, and Modeling. The realization has

been divided into these same three realms. Each realm

corresponds to the research sub-questions defined in

chapter 1.

3.1 Harvesting & Preprocessing

The first step before it is possible to start the CRISP-

DM Data Understanding, is the collection and

preparation of data. To mimic a real-life situation, the

racing simulator, as described in 3.1, is used for

running the F1 2020 game. Within this game, the

option for sharing telemetry is turned on. The

harvesting of data has three steps. The first step is the

sending of data. As the API provides this

functionality, there is little control over the formatting

of the data sent. The F12020 game handles the correct

sending over a UDP connection to an available client

on the same network. For ensuring that the

connectivity is over the same network, a mobile

hotspot is set up on the client-side and the racing

simulator is connected to this mobile hotspot. The

second step is to retrieve the data on the client-side

and process this into readable data. This client-side is

specifically built and designed for this project and

therefore we do have control over the data and its

corresponding formatting. The third step is to filter

the relevant information and parse this to a database.

The scheme of how the ATS system and its

components interact is displayed in figure 2.

When data is retrieved from the API, the client

decodes the data stream and processes the data into

information objects. As denoted in section 2.2, the

different data packets arrive asynchronously. To merge

arriving packets into one data object, an object buffer

is created with a 7-millisecond lifespan. Throughout

the lifespan of this buffer, all retrieved data is

combined into the same object and redundant data is

overwritten. These objects are then formatted into a C#

directory to later be formatted into a JSON object.

Figure 2: The ATS configuration.

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A connection is made between the client and the

Firebase database. Once the end of the lifespan of a

data buffer is reached, the JSON object gets parsed to

the Firebase FireStore database. Accordingly,

Firestore responds with an approval message or an

error. The error contains information and instructions

on how to proceed. The approval message validates

the arrival of data into the database.

The database is divided into three collections. The

first collection (“Collection 1”) contains all the

gathered Test data based on the collection

construction extracted from the packets as denoted in

section 2.2. On the contrary, the third collection

(“Collection 3”) contains all the gathered Training

data. The second collection (“Collection 2”) contains

real-time telemetry updates and therefore is updated

every 0.5 milliseconds based on the incoming stream

of data from the Racing Simulator.

Considering that the goal of this first realm is to

gather and process data from the game into a data

collection, the desired result is a database filled with

telemetry data, session data, and test data. Looking at

the implementation of this section, this goal has been

achieved, and by the end of the developments of this

section, a database was available to continue to the

next step within the overall

3.2 Understanding & Learning

The second step within the CRISP-DM methodology

is the understanding of the obtained data. In essence,

the goal of the system is to provide the driver with

information about what to do best at specific parts and

segments of the track. It is important that the driver

must intuitively learn to act in a certain way, for

which the boundaries must be defined by this

algorithm. Therefore, when constructing the model

for understanding the gathered data, a breakdown of

tasks is required. For the implementation of the

“Understanding & Learning” part within this

research, a division of task categories has been made,

this division is displayed in table 1.

This part of the realization requires a strong

object-oriented structure and hence the Java

Programming Language is the language of use.

Starting at the first set of categories, the first step is to

retrieve data from the database. Consulting the

Firebase integration documentation, reading data is

done by obtaining the collections and retrieving the

corresponding documents per collection. Within the

experiments, it is expected from the participants to

first perform 8 laps under training circumstances.

This data is stored under the Training data collection.

The next step in the process is to simplify the

retrieved data and group the data together. The

parameters for simplifying data are the current

timestamp in seconds on the track and the distance

Table 1: Task division.

1) Retrieval of Data Read Firebase

2) Simplification of

Data

Create data markers per

condition X/Y*

3) Grouping of Data Group on conditions

X/Y

4) Construction of

Summaries

Calculate summary**

and averages Marker

roup

5) Classify Marker

Groups (MG)

Within each group,

classify value on

ormal Distribution

6) Recreation of

Trackline

Create ideal trackline

from highest

classification per MG

* X is defined as the distance ration, Y is defined as the time in lap

** The summary correlates to the Five number summary altogether

with the standard deviation and mean

ratio in percentages. The level of significance is in

milliseconds for the timestamp and one decimal after

the comma for the distance.

The next step within the process of understanding

& learning is to create the corresponding summaries

for defining the ideal telemetry set per marker. This

is done only for the marker with the distance ratio, as

this marker defines the leading track line correlated to

the telemetry. The summary consists of the distance

ratio, the mean wheel angle at this distance ratio, the

mean throttle/brake ratio at this distance ratio, and the

modus of the gear at this distance ratio.

After the mathematical summary has been

created, the markers get labeled with a classification.

This classification is built upon the Normal

Distribution where the critical values are defined by

the Z-Values derived from a distribution with a level

of significance of 0.05. Accordingly, the obtained Z-

Value for a 𝞪

total

= 0.05 is equal to

𝞪

upperTail

= 0.025 ⇒ 𝜇+ 2𝜎

𝞪

lowerTail

= 0.025 ⇒ 𝜇 − 2𝜎

Yielding the following criteria for the

classification:

For

mula

𝜇

−2𝜎

𝜇 −𝜎 𝜇 𝜇 + 𝜎 𝜇

+2𝜎

Clas

sifier

dLow

Mid

High

Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning

263

For which 𝜇 is the calculated marker average per

parameter of the marker and 𝜎 is the corresponding

standard deviation from this average.

After the dimensions of the classification have

been defined and the markers have been classified,

the entire dataset, as retrieved from Firebase, gets

classified on the basis of the aforementioned

classification criteria. Subsequently, the lower

classified data markers get removed from the dataset,

ensuring only “Mid”, “MidHigh” or “High” classified

markers and data points within the dataset. The next

step is therefore constructing a new trackline based on

the highest classified data points. This recreated ideal

trackline is defined as the “Advised Trackline for

maximum performance”. Throughout the entire

session, this process is repeated, improving the ideal

trackline per newly created or updated marker.

Throughout the research and the experiments, the

driver will face a certain learning curve that might

influence the research results. To overcome this

learning curve and therefore to minimize the effect of

this learning curve, the participant is asked to drive 8

laps before the test. Throughout these laps, the

participant will expose the learning curve by means

of increasing marker classifications. Once the system

recognizes a stabilization within the graphical

representation of the participant’s output, the learning

curve gets identified as all the output before the

stabilization. Accordingly, the data gets removed

from the training dataset, and the participant’s

learning curve is eliminated. However, the markers

classified as Mid, MidHigh, and High will remain in

memory for the improvement of the ideal trackline.

3.3 Communication & Visualisation

The third step in the CRISP-DM methodology is the

modeling of data. Displaying feedback is done

through a visualization dashboard. As mentioned

within section 3.2, the aim of the visualization is to

inform the driver about his current positioning on the

track, his current performance compared to the

advised line, and the improvements the driver has to

take to improve his performance without causing too

much distraction. Hence the visualization must be

simple and easy to understand from out of the corners

of the driver's eyes. The criteria for the visualizations

are that the colors must be distinguishable and the

information must be recognizable. For the steering

angle, a two-sided horizontal histogram is used to

denote the rate of change that needs to be applied to

the current steering angle. The Brake and Throttle

work according to a vertical bar chart that turns green

when too little pressure is applied and turns red when

too much pressure is applied. The visualization tool is

developed using the Processing 4.0 Beta 5 Library

within a Java Application. A snapshot of the

application is provided in Figure 3.

Figure 3: A snapshot of the visualization tool.

4 SYSTEM VALIDATION &

RESULTS

Within this section, the analysis of the results is

conducted. Before this analysis can take place, an

experiment is set up with a certain number of

participants. A single experiment consists of two

drivers, a potentially good driver, and a potentially

worse driver. The better driver is asked to participate

in the experiment first. Subsequently, the worst driver

is asked to participate in the experiment as the second.

Although not desired, it is important to anticipate a

situation wherein not all participants have an equal

amount of knowledge of racing simulators.

Therefore, to reduce the effects of this knowledge

gap, the experiments are divided into 5 sections:

1) Participant Briefing & installation

2) Training Session

3) Test Session

4) Survey

5) Open Discussion

The experiment starts with a brief introduction to the

project and a brief introduction to the experiment.

Once these introductory briefings are done, the

participants start a training session wherein he/she has

to drive 8 full laps on Circuit Zandvoort. There will

be no feedback given to the participant. The data is

collected and used for preparing the predictions that

will be shown to the participants in the test session.

During the Test session, the driver is once again asked

to drive 8 full laps, this time with visualizations and

feedback. Due to GDPR regulations, details about

participants will not be shared.

4.1 Results

For this research there was room to conduct three

experiments. The analysis of the experiments has

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264

been divided into four parts. The first part is on the

harvesting of data and the reliability of the created,

ideal, trackline. Secondly, an analysis of the overall

performance of drivers is done by depicting the R-

Squared values of the Test sessions against the R-

Squared values of the Training Sessions for every

participant. Thirdly, the performance based on the

brake ratio, the throttle ratio, and the steering angle is

analyzed based on the initial training dataset and the

test dataset. Lastly, a brief analysis of the feedback

system is done. This is done based on the responses

to the survey.

To visualize the power of big data and process

mining, the effect of a high data acquisition rate is

denoted against the reliability of the created advised

trackline. By displaying the number of timestamp

markers created per lap, it is easily seen when and

where the data packets have dropped. To overcome

the amount of dropped data, more data must be

gathered. As depicted in figure 4, once the total

number of laps driven increases, the total coverage of

the timestamps increases, leading to a higher

accuracy per marker. From this dataset, a generalized

model can be constructed wherein each required

timestamp is covered by the total data set. From this

generalization, a reconstruction of the trackline can

be created.

Figure 4: The data markers coverage per timed lap,

depicting the packet drops.

4.2 Driving Performance

When the goal of training is to improve multiple

drivers, along the same progress line, the variation in

data is the most important factor. More variation

means a larger difference in performance and

therefore a larger difference in abilities, confidence,

and skills. The analysis is based on the R Linear

Regression model wherein the severity of variation

within the dataset is denoted in R-Squared. R-

Squared is in the context of this research defined as

the statistical measure of how close the data fit the

generated regression line. The datasets recorded per

driver showed decreasing classification levels the

longer the session took. From this can be concluded

that due to the effects of fatigue and exhaustion,

drivers did perform less at the end of a session

compared to the beginning of the session. For the

reliability of the analysis, only laps 3 and laps 5 are

taken into account.

The R-Squared is defined as the depiction of the

distance ratio against the amount of time on track.

The regression line, therefore, is the ideal line

wherein the 100% distance ratio is reached in the

most average amount of seconds. The R-Squared

value is calculated using the formula:

𝑅



 1−

𝑆𝑢𝑚 𝑆𝑞𝑢𝑎𝑟𝑒𝑑 𝑅𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛

𝑇𝑜𝑡𝑎𝑙 𝑠𝑢𝑚 𝑜𝑓 𝑆𝑞𝑢𝑎𝑟𝑒𝑠

Figure 5: The R-Squared value as a rate of variation on

Lap 3.

Figure 6: The R-Squared value as a rate of variation on

Lap 5.

When analyzing Figure 5, the most visible

difference between the training and test data is that

there are fewer peaks and therefore fewer outliers

within the datasets during the test session compared

to the training session. Moreover, the values seem

closer to each other. This same phenomenon seems to

be present in Figure 6, where the analysis on lap 5 is

depicted. This formula yields the following table of

R-Squared values against the corresponding lap and

session type:

Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning

265

Table 2: R

values for lap 3 and lap 5 for both session types.

Lap 3 Lap 5

Training Session R

= 0.977665 R

= 0.979416

Test Session R

= 0.992191 R

= 0.988712

From this table it can be deduced that in any case,

the training session had less variety within the data

samples, although there is only the slightest

difference. Nonetheless, This yields that the testing

session had an additional factor in play that caused

this slight increase in overall performance.

4.3 Car Handling

The second performance analysis method is based on

the telemetry data retrieved from the real-time

database and the ideal telemetry calculated by the

learning system. Before the experiments started, the

drivers were grouped based on their experiences with

driving and racing simulators. According to these

groupings, driver duos were created. Each duo

consisted of a presumed experienced driver and a

presumed inexperienced driver. The aim of this

division between the participants opened options for

amplifying the effects of the training to gain the

maximum insights as possible during the analysis

phase.

When looking at the overall race pace of the

drivers in figure 7, it is seen that almost all drivers

improve upon their average speed. Within this

context, a higher average speed yields lower lap times

and hence a more efficient drive.

Table 3: The difference is race pace denoted in percentages

[%].

Driver

name

Avg Pace

during

Training

Session

[km/h]

Avg Pace

during

Test

Session

[km/h]

Difference

rate in

percentages

[%]

Pla

er 1 199.14 197.59 -0.808

Pla

er 4 174.58 178.43 +2.203

Player 2 199.80 200.79 +0.469

Pla

er 5 179.71 186.79 +3.938

Pla

er 3 197.78 205.01 +3.659

Player 6 178.70 189.73 +6.174

From table 3, it can be deduced that the rate of

change of the inexperienced driver correlates to the

rate of change of the experienced driver. Meaning that

if the experienced driver barely increases, the rate of

change for the inexperienced driver will be low due

to the low quality of the data. If the experienced driver

improves a lot, the quality of the data is high and

hence the inexperienced driver can benefit from this

set of highly classified data, meaning that the

feedback would become more accurate and reliable.

Figure 7: The difference in race pace with and without

feedback.

4.4 Brake Throttle Differences

Given the large difference in overall pace and driver

performance of duo 3; red, analyzing the data of this

duo returns the best visible effect of the duo training

program. In figure 8, the difference in brake and

throttle performance of player 6 has been depicted,

wherein the distinction has been made between the

session type (training, test). During the training

session, it is seen that there is much fluctuation in the

throttle. This leads to less time on the maximum

throttle and therefore less overall speed. These

fluctuations can be explained by the level of

confidence of the driver.

This same principle counts for the brake.

Comparing the results of the training session to the

test session, it is seen that within the test session, the

driver has much more confidence as there are fewer

fluctuations in the driver’s brake and throttle

handlings. This implies that the driver has more

understanding of the situation and hence can better

control the car to operate at maximum performance.

Moreover, it is seen that the driver is making less

use of the brakes and therefore makes more use of the

friction of the engine to slow down, implying that

more speed and more pace is carried throughout the

track, leading to more efficient handling of the car.

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266

Figure 8: The brake and throttle performance during the

training and test sessions.

4.5 Steering Efficiency

During the training session, the player has a lot of

fluctuation in the steering wheel throughout the lap.

Within the context of the steering angle, fluctuations

mean the rate of corrections required to operate the

car. Hence, more fluctuations imply more corrections

and therefore less control of the car and the situation.

Comparing the results from the training session and

the test session, it is evident that the number of

fluctuations has decreased, implying that the driver

had more control over the car. Moreover, it is seen

that the steering angles remain more consistent over

the track segments, meaning that the cornering gets

longer, yielding more pace at the end of the corner,

yielding improved exits.

Figure 9: The steering angle during the training and test

session.

5 DISCUSSION

When willing to validate the results obtained during

the experiments, it is of high importance to be

realistic about the effectiveness, accuracy, and

reliability of the system and the tests. Therefore a

disclaimer must be made. While the results, as

introduced in chapter 4, seem promising and

effective, more statistical testing needs to be done.

5.1 Recreation of Ideal Trackline

The corresponding research subquestion that belongs

to this topic of the research is, as denoted in section

1.3: “To what extent is it possible to recreate an

artificial trackline built upon the basis of the highest

performances throughout the track?”. When trying to

answer this research question, the results that section

4.1 yielded showed that approximately 8 laps were

required to fully cover every second and every driver

meter of the track. From the data collected through

the racing simulator, it is possible to reconstruct the

events, with regard to every parameter of the car as

provided by the Codemasters API, that occurred

during the moment on the track. In this way, a data

collection can be created to artificially regenerate the

track and the position of the car on the track while

having every parameter required or not required in

mind. Therefore, the extent to which it is possible to

recreate the trackline is endless as long as the

database allows data to be captured.

5.2 Translation of Data into Feedback

The second question that needs to be answered before

reliable feedback can be provided to the driver, is the

question on the translation of data into advice based

on the current telemetry. The corresponding research

question to be answered is “How can this artificial

trackline be translated into terms of required

telemetry changes to guide towards this trackline?”.

The translation needs to happen for three parameters

only, as denoted by section 3.3. While these three

parameters are based on a series of calculations to

determine which values of the parameters are actually

the value to display, these parameters are easily

interpreted as single, rational values. These values

can then be translated into advice per marker, as

created by the learning system, and automatically be

bound to represent the marker in terms of the

telemetry settings.

5.3 Projection of Feedback to Driver

Evaluating the current visualization of the data, it is

important to take into consideration the parameters

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that need to be available to the driver. When looking

at the corresponding research question, the question

yields: “How can these telemetry changes be

communicated to the driver in the most effective

manner?”. Throughout the research, it was concluded

that not all types of feedback were ideal within the

context of this application, e.g., acoustic feedback

was labeled as useful for quick updates but not for

continuous feedback. As opposed to this statement,

the participants did agree that for continuous

feedback, visual feedback would be better, provided

that the manner of presenting this feedback was more

subtle. A potential implementation of this feedback

system would be integration within the F12020 game

or a virtual reality overlay. In this end, this project

remains a work-in-project and hence further research

is required before practical implementation

5.4 Limitations

Having in mind the scope of this research, up until the

current standing, the project seems promising and

yields great results. Considering that simulators are

already widely used for training professional drivers,

an additional layer of training and, eventually,

protection is seeming to be the way to conquer the

checkered flag.

Considering the minimal requirements for setting

up this research, many limitations have come to play

during the project. These limitations cover a broad list

of items that need to be discussed when willing to

redo or expand this project. The actual research

limitations will be discussed in the next section. The

items that this section will cover include:

• The availability of materials

• The budget cap

• Domain Experts

• The reliability of the UDP protocol

• The database limitations in contrast to the data

collection size

• The limited research on the Formula One Topic

• Limited availability of drivers

6 CONCLUSION

The aim of this paper is to find an optimal way of

enhancing driver performances by adjusting the

training according to gathered data on earlier

achieved performances. This was done in a process of

three steps. The first step was to harvest data on the

performances of drivers within a team or cluster. This

was done using the Racing Simulator and principles

of process mining. The data was stored in a database

for later analysis. The second step was to analyze the

gathered data with the main purpose of learning the

track boundaries, the telemetry boundaries, and

understanding the abilities of the driver. This was

done using conformance checking, basic principles of

statistics, and linear regression. Lastly, the analysis of

the data retrieved from the learning model was

translated into valuable feedback and displayed to the

driver through a feedback system.

When analyzing the data gathered by the ATS

system, several factors play a role to determine

whether a created marker is of high value. After this

classification of data markers has been made, a

training set is created from the currently stored data

with which the system trains itself to recognize

patterns. According to these created patterns, the

system builds advice per second and per distance ratio

on the track and bundles this with the corresponding

telemetry information. Moreover, the system reads

out the real-time telemetry database to link the current

behavior of the driver to a previously occurring event

or a generated marker to optimize the action, and

eventually the performance, of the driver.

The results of the experiments conducted with

three duo’s drivers were promising. Almost all

drivers showed an increase in performance and a rise

in confidence. Fewer fluctuations were observed at

the steering wheel, implying more control over the car

and a higher understanding of the abilities of the car

and above all, the abilities of the driver. Additionally,

more peaks in the use of the throttle were observed

while the use of the brakes decreased, resulting in

more overall pace and performance.

Nevertheless, while these results do imply an

effective training concept, the statistical backbone of

the project is weak. More experiments must be

conducted with a larger sample size to guarantee the

effectiveness of the training.

In conclusion, it is not yet possible to guarantee

that this manner of training works. The initial concept

of the training method appeared to be effective and

pervasive, however, the system lacks statistical

coverage to prove that this way of training athletes

guarantees an improvement in performance.

6.1 Practical Recommendations

It can be said that this training method shows

potential as the results obtained look promising.

However, to improve the system to make it

waterproof, some recommendations must be made.

The main recommendation to be made is the system

that all participants seemed to have difficulties with;

the feedback system. As this feedback system is the

main interface for the drivers to interact with, this

system must be either optimized in a way that it does

not form a distraction or the feedback system must be

implemented according to the feedback received from

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the participants. In further research, I would

recommend redesigning the feedback system in a way

that is more visible to the driver with less effort.

Furthermore, it is of high importance to keep the

information even simpler so that the driver can see or

feel in a blink of an eye what is expected. Another

recommendation that I deem important is the speed of

the database. While the database showed an

impressive amount of speed and functionality, the

system lacked a bit behind due to the congestion

errors that were present by default. The internet

connection and the database configuration seemed to

be a bottleneck throughout the entire process. Perhaps

in future studies, a local database could be

implemented to overcome these issues.

Additionally, the learning and analysis method is

currently based on the normal distribution. While this

classification method seems to work for this context,

it is not always reliable. If a car crashes along the way,

the entire lap gets classified as a low marker.

Neglecting the time that a car is lacking in this

situation, the driver might still recover and increase

his pace. This increase in pace is currently not

counted towards the final classification and hence the

data is discarded. Having too many of these data

points might corrupt the data. To overcome this, a

fully functioning deep learning algorithm can be

implemented to recognize events like crashes.

6.2 Future Work

To exploit the effectiveness of this training method,

these recommendations must be taken into account.

Improvements must be made to increase the

reliability and accuracy of the system. Moreover, by

conducting more user tests, a statistical and scientific

backbone can be created for the training method.

Additionally, although the initial concept relied

on machine learning and deep learning principles, the

final concept within the scope of this research barely

made use of these concepts. For future development

of this project, machine learning and/or deep learning

could be exploited to better understand the obtained

data and perhaps give suggestions beforehand instead

of in real-time.

Lastly, the method of displaying information must

be changed. As denoted in the recommendation

section, another manner of providing feedback must

be implemented to gain the maximum result while

keeping the level of distraction low.

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