Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22

Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925

Volumen 6 (4): e328. Octubre-Diciembre. 2023. https://doi.org/10.37636/recit.v6n4e328

ISSN: 2594-1925

Research article

Voice communication module for automotive instrument panel

indicators based on virtual assistant open-source solution -

Mycroft AI

Módulo de comunicación de voz para indicadores de un panel de instrumentos

automotriz basado en la solución de asistente virtual de código abierto - Mycroft AI

Ricardo Hernández Mejía1, Francisco Javier Ibarra Villegas2, Caín Pérez Wences2

1Posgrado CIATEQ, A.C., Av. Nodo Servidor Público #165 Col. Anexa al Club de Golf, Las Lomas, 45131 Zapopan, Jalisco, México

2CIATEQ, A.C. Centro de Tecnología Avanzada, Santiago de Querétaro, 76150, Querétaro, México.

Corresponding author: Ricardo Hernández Mejía, Posgrado CIATEQ, A.C., Av. Nodo Servidor Público #165 Col. Anexa al Club de Golf, Las

Lomas, 45131 Zapopan, Jalisco, México. E-mail: richernandezm@gmail.com. ORCID: 0009-0005-2384-1464.

Submitted: 19 de Septiembre del 2023 Accepted: 19 de Septiembre del 2023 Published: 5 de Octubre del 2023

Abstract. - This work was originated from the increasing interest in several industries to implement voice based virtual assistant solutions

powered by the Natural Language Processing field of study. This work is focused on the automotive industry Human Machine Interface

related products, specifically the Instrument Panel. Nowadays people are constantly using virtual assistants like Google Assistant, Alexa,

Cortana or Siri on their electronic devices. Furthermore, 31% of cars have a built-in virtual assistant, for example Ford uses Alexa, Merced-

es-Benz and Hyundai use Google Assistant, BMW and Nissan use Cortana, GM uses IBM Watson, Honda uses Hana and Toyota uses YUI.

Apart from the proprietary solutions described earlier, there are also contemporary open-source generic solutions available on the market,

such as Mycroft AI which stands out from other technologies due to ready to deploy, well documented, simple installation on a Linux PC or

RPI SoC, and simple execution. This paper presents a way to use Mycroft AI as an alternative to add artificial intelligence-based voice

assistance to applications in the automotive domain. The voice communication module presented here drives notifications related to three

different entities: seat belt, fuel level and battery level, all of them are telltales present in any automotive Instrument Panel. Since the Mycroft

AI design approach is based on Human Centered Design (HCD), the voice communication module presented here provides real user

experience (UX) based design. As a conclusion, Mycroft AI demonstrates great potential as an alternative to add voice assistance to

automotive industry Human Machine Interface related products. About future work, due to the fact that Mycroft AI is based on Python, there

are many possibilities for connecting and expanding the voice communication module by using countless Python libraries in order to import

and process any type of information, in any format or source, for example the information from communication technologies like CAN, LIN,

Ethernet, MOST, GPS or any other device or technology in order to create comprehensive automotive solutions.

Keywords: Instrument panel; Virtual assistant; Voice communication module; Mycroft AI; Human centered design; User experience.

Resumen. - Este trabajo se originó del creciente interés por parte de diferentes industrias para implementar soluciones de asistente virtual

basado en voz impulsadas por el campo de estudio del Procesamiento del Lenguaje Natural. Este trabajo está enfocado en los productos

relacionados a la Interfaz Humano Máquina de la industria automotriz, específicamente el Panel de Instrumentos. Hoy en día las personas

usan constantemente asistentes virtuales como Google Assistant, Alexa, Cortana o Siri en sus dispositivos electrónicos. Más aún, 31% de

los autos tienen un asistente virtual integrado, por ejemplo, Ford usa Alexa, Mercedes-Benz y Hyundai usan Google Assistant, BMW y Nissan

usan Cortana, GM usa IBM Watson, Honda usa Hana y Toyota usa YUI. Aparte de las soluciones de marca registrada descritas

anteriormente, también hay soluciones genéricas de código abierto contemporáneas disponibles en el mercado, tales como Mycroft AI que

se hace notar por sobre otras tecnologías por características como listo para usar, bien documentada, instalación simple en una PC Linux

o RPI SoC, y una ejecución simple. Este artículo presenta una manera de usar Mycroft AI como una alternativa para agregar inteligencia

artificial basada en asistencia de voz a aplicaciones en el dominio automotriz. El módulo de comunicación de voz presentado aquí maneja

notificaciones relacionadas a tres diferentes entidades: cinturón de seguridad, nivel de gasolina y nivel de batería, todos ellos son

indicadores virtuales presentes en cualquier Panel de Instrumentos Automotriz. Dado que el enfoque de diseño de Mycroft AI se basa en

Diseño Centrado en el Human (HCD), el módulo de comunicación por voz presentado aquí provee un diseño basado en experiencia de

usuario (UX) real. Como conclusión, Mycroft AI demuestra gran potencial como una alternativa para agregar asistencia de voz a los

productos relacionados a Interfaz Humano Máquina de la industria automotriz. Acerca del trabajo a futuro, debido al hecho que Mycroft AI

está basado en Python, existen muchas posibilidades para conectar y expandir el módulo de comunicación por voz a través del uso de

innumerables bibliotecas de Python para importar y procesar cualquier tipo de información, en cualquier formato o fuente, por ejemplo la

información proveniente de tecnologías de comunicación tales como CAN, LIN, Ethernet, MOST, GPS o cualquier otro dispositivo o

tecnología para crear soluciones automotrices integrales.

Palabras clave: Panel de instrumentos; Asistente virtual; Módulo de comunicación por voz; Mycroft AI; Diseño centrado en el humano;

Experiencia de usuario.

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1. Introduction

There is a growing interest in the field of NLP

study in academia, as several industries are

deploying virtual assistant solutions. [1] reports

27% of people have whether Google Assistant,

Alexa, Cortana or Siri, the smartphone is as 85%

high in adoption over intelligent speakers,

tablets, laptops, smart TVs, wearable technology,

and home automation, nevertheless, a remarkable

31% of cars nowadays have a virtual assistant. In

the automotive industry, [2] shows pairs of

manufacturers and virtual assistants, this is Ford

uses Alexa [3], Mercedes-Benz [4] and Hyundai

[5] use Google Assistant, BMW [6] and Nissan

[7] use Cortana, GM uses IBM Watson [8],

Honda uses Hana [9], Toyota uses YUI [10].

From the academy and research communities,

related relevant patents provide innovation

trends, on the one hand, Automotive Virtual

Personal Assistant [11] which is a system that

actively monitors the car state to provide relevant

notifications, and on the other hand, Proactive

Virtual Assistant [12] which evaluates user’s

information to provide suggestions and perform

actions in advance. Finally on worldwide events

like the Consumer Electronics Show, technology

and innovations from carmakers are presented as

well, like the Mercedes-Benz User Experience

Hyperscreen [13] which apart from multiple

displays a virtual assistant is integrated, by Hey

Mercedes command car information is retrieved,

and taking into consideration GPS location,

information of nearby restaurants, parking lots,

between others is provided to the driver. It is well

known that open-source products have

characteristics such as low or non-existing cost

for usage and distribution depending on the

license type, high quality, security, open access

and flexibility to modify its components,

furthermore collaboration and innovation are

present due to development communities’

support; so, the virtual assistant solutions are not

the exception to the rule. Based on the earlier,

this work presents an investigation result in a

comparison table of relevant features of

contemporary open-source virtual assistant

solutions, besides a detail of the chosen solution

Mycroft AI’s components, algorithms, and

methods is provided. Afterwards, the steps to

create a Mycroft AI skill or application are

presented, followed by the customization of

intent, dialog and entity files for automotive

instrument panel’s indicators seat belt, fuel level

and battery level. The main application design

relies on dynamic behavior diagrams, a state

machine, and a sequence diagram, to create a

base product of a voice communication module

for automotive instrument panel indicators based

on Mycroft AI as the final goal. Finally,

achievements, contributions and future work are

listed and discussed.

2. Background

A wide range of open-source tools and

technologies related to virtual assistance are

available in the market, investigation was

performed on sites such as makezine [14] where

free and private voice assistants are compared

based on open source architecture components,

medevel [15] in which open-source technologies

and platforms of popular voice assistants are

analyzed, yourtechdiet [16] which lists project’s

origin and up-to-date status of best open source

voice assistants, and finally libhunt [17] which

reports virtual assistant solutions’ popularity

based on activity, commits on corresponding

repositories and mentions from development

communities, based on the earlier, the “Table 1.

Contemporary open-source virtual assistant

solutions” was created, the table shows a

comparison between different contemporary

open-source virtual assistant solutions and their

most relevant characteristics. Mycroft [18] stood

from the crowd due to ready to deploy, well

documented, simple installation on a Linux PC,

and straightforward execution.

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Table 1. Contemporary open-source virtual assistant solutions.

Assistant

Os/Hw

Prog.

Language

Popularity

Internet

required

Privacy

Customi

zation

Document

ation

Mycroft

Linux, RPI

Python,

Bash

Leon

Windows,

Mac

Node.js,

Python,

Http

Rhasspy

RPI

Docker,

Python,

Shell

Jasper

RPI

Python

Almond

Linux,

Web

Javascript

OpenAssi

stant

Windows,

Linux,

Mac

Own SDK

LinTO

RPI

Docker,

Python,

Bash,

C++, Java

Aimybox

Android,

iOS

Apache

2.0

Kalliope

RPI,

Linux,

Android

Python,

Rest, Bash

2.1 Why use Mycroft?

Mycroft is presented on IEEE's Entrepreneurs in

consumer Electronics [19] as an open-source

software platform which integrates technologies

that have significantly improved in recent years

such as speech recognition, text to voice,

command processing, etc., all these technologies

allow to add voice assistance powered by

artificial intelligence to any application executed

on laptops, speakers, Raspberry PIs and cars.

A successful deployment of Mycroft is [20] in

which an intelligent robot assistant is created to

handle smart homes for the elderly.

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Over other solutions Mycroft [21] presents itself

as:

 Open source: The Mycroft code can be

analyzed, copied, modified, and distributed,

these cannot be said for Alexa, Google Assistant,

Cortana, Siri which are black boxes, contents is

hidden and protected by commercial licenses.

 Respectful of users’ privacy: Voice

recording works only if user grants permission.

 Multiple hardware compliant: RPI,

Android, Linux PC.

 Light: Designed to be executed on low

cost, low power, and low resources hardware.

 Community oriented: Vibrant, committed,

and helpful community.

2.2 Modular Mycroft

Mycroft implements Voice Stack components

[14] as part of an open-source virtual assistant

architecture, these components can be

configured, personalized, started and stopped

independently, their openness and flexibility is

the main advantage of Mycroft against the

commercial and other open-source counterparts.

 Wake Word Detection: “Hey Mycroft” is

default, customized through [22]. Due to the

simplicity to configure a new Wake Word

through phonemes on a text configuration file

based on CMU Dictionary of sound, it was

decided to use PocketSphinx [23] part of

CMUSphinx [24] originally based on the

SPHINX-II [25] speech recognition system, this

system achieves improved unified acoustic and

language modeling through normalized feature

representations, multiple-codebook semi-

continuous hidden Markov models, between-

word senones, and multi-pass search algorithms.

Software Precise can be used to provide higher

precision on Wake Word detection at the expense

of demanding training a NN on large audio

sequences.

 Speech To Text: Google STT [26] is the

default engine, deep learning progress on voice

transcription makes possible models like LSTM

RNN [27] perform remarkably on the speech

recognition domain and further text transcription.

 Intent Parser: Adapt [28] is the default

software developed by Mycroft AI to identify

utterances or commands as machine readable

data structures after parsing the natural language

text input. Software Padatious [29] can be used

to provide higher precision on utterance

detection at the expense of demanding training a

NN on required phrases.

 Text to Speech: Mimic [30] is the default

software, developed by Mycroft AI along with

VocaliID, Mimic is based on the open-source

text to speech synthesis engine CMU’s Flite,

voice synthesis is achieved by Classification and

Regression Tree and Finite State Transducer

algorithms. Google TTS [31] can be also used.

 Mycroft Skills: Mycroft AI applications,

e.g., timers, alarms, whether, time, date, etc.,

custom skills can be developed with Mycroft

Skills Kit (MSK) support and Python

programming language.

2.3 User Centered Design

Mycroft's design and development approach is

driven by user's needs, the philosophy also

known as User Centered Design (UCD) a concise

version of Human Centered Design (HCD)

which turns around observe and understand

users, assures useful, understandable, pleasant,

and enjoyable products to interact with,

essentially the final goal of the Norman's User

Experience [32].

Mycroft makes use of the Design Thinking [33]

method, which synthesizes the problem handling

as follows:

When _______,

I want _______,

So, I am able to _______,

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This follows the Mycroft's application flow.

 What words will be used?

 What will be the answer to provide?

 What information will be needed?

 What dependencies are required.?

3. Methodology

After the hardware to work with is chosen

whether it is RPI, Android or Linux PC, in order

to use Mycroft AI, steps in [18] must be followed

to install and set all the dependencies needed to

successfully run the software solution, generic

tests are provided to make sure the solution is

properly installed and dependencies working

well, this is especially relevant for the

microphone and speakers peripherals and

corresponding drivers, just in case of trouble,

useful help is provided by Mycroft AI with a

troubleshooting section [34].

The “Figure 2. Mycroft AI flow to create a Skill”

shows the steps to create and modify the Skill to

meet custom needs.

Figure 2. Mycroft AI flow to create a Skill.

3.1 Mycroft Skill Kit

Mycroft Skills Kit (MSK) utility is installed

along with Mycroft with the objective to

facilitate the creation, upload, and upgrade of

skills in the corresponding local directories or

repositories. mycroft-msk create is the console

command used to run MSK and execute an

interactive script to ask and answer information

to generate a skill skeleton in the form of a

template.

3.2 Creation of Automotive Telltales Skill

An automotive Instrument Panel (IP) presents to

the driver different ECUs's signals visually or

auditory, this work considers three main telltales

which are part of the safety relevant telltales

according to the National Highway Traffic

Safety Administration (NHTSA) [35]: seat belt,

fuel level and battery level.

For example, for seat belt telltale, applying the

Design Thinking approach previously presented,

we get the following output:

When seat belt status is queried and unfastened,

I want to be provided a suggestion to fasten the

seat belt,

So, I am able to achieve a trustful trip.

“Table 2. Mycroft Skill Kit script for Skill

Telltales” shows the mycroft-msk create

command result for seat belt telltale.

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Table 2. Mycroft Skill Kit script for Skill Telltales.

Script question

Answer given

Utterances

What is the car seat belt status

Dialog

Car seat belt is fastened

Short description

Telltale status / Indicator level retrieval

Long description

Skill to retrieve instrument panel's current telltale

status or indicator level

Author

Hernandez Ricardo

Tags

None

Finally, MSK's script asks for GitHub's

repositories creation for the skill, this will be

required only if the skill will be published in the

Mycroft's marketplace. In our case, it is enough

the MSK's output, this is the Skill Telltales

located in local directory /opt/mycroft/skills,

“Table 3. Mycroft Skill Telltales output” shows

output files and directories.

Table 3. Mycroft Skill Telltales output.

Item

Description

Locale

Directory containing files (intent and dialog) for

every language supported, en-us for English as

default.

__init__.py

The skill python-based core to import libraries,

define class which inherits from Mycroft Skill to

work with voice, define own methods to handle

intents and dialogs, create skill's class instance and

its execution.

README.md

Skill's human readable information, provided in

MSK's script, short and long descriptions, author,

category.

settingsmeta.yaml

Parameters of Mycroft's profile stored at

https://sso.mycroft.ai/home.mycroft.ai, like date,

time, time measured, location, voice type, etc.

manifest.yml

External software dependencies if any.

3.3 Intents, Dialogs and Entity files

Within Locale directory it is found intent file and

dialog file which contains phrases originated in

MSK's script, later by manually editing this pair

of files and following the Mycroft’s guidelines

and rules, custom utterances and dialogs were

added to these files. Besides, file telltales.entity

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was manually created to provide flexibility, a

wild-card to handle different kind of phrases for

each telltale, the entities are used in both, intent

and dialog files. Referenced files and

corresponding contents are summarized in

“Table 4. Intent, Dialog and Entity files”. Facts

about dialogs are on the one hand, that they are

randomly chosen by Mycroft to simulate a more

natural impression, and on the other hand in this

work dialogs are intentionally incomplete to

handle apart each telltale's status or level through

python code. The result is the user experience’s

feedback functionality in action.

Table 4. Intent, Dialog and Entity files

File

Content

telltales.intent

Car {telltale} status

Current car {telltale} status

What is the car {telltale} status

Status of car {telltale} status

(Give | Tell) me the car {telltale} status

telltales.dialog

{telltale} is

Car {telltale} is

Current car {telltale} is

telltales.entity

seat belt

fuel level

battery level

Furthermore, a sample of user experience’s

feedforward functionality, this is, the helpful

information provided to the user to decide what

to do next, is exemplified as a simulated car trip

in three different modes. Every trip mode

represents a different level of feedforward

functionality detailed in “Table 5. Trip modes for

feedforward functionality”.

Table 5. Trip modes for feedforward functionality

Trip mode

System’s behavior description

Notification Mode

Telltale’s status or level is provided with simple

sentence and no more. Decision of what to do

next relies completely on the user.

Suggestion Mode

Telltale’s status or level is provided with context

information sentence about the car’s location.

Even decision of what to do next still relies on

user, on this mode the decision is facilitated.

Action Mode

Telltale’s status or level is provided with context

information sentence about the car’s location.

Decision of what to do next relies completely on

assumed autonomous car.

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A simple state machine model was created to

depict available Trip Modes, this is presented in

“Figure 3. Trip Modes state chart”. To start a trip

and enter one of the Trip Modes available,

utterances are introduced by adding intent files

presented in “Table 6. Trip modes intent files”.

Figure 3. Trip Modes state chart.

Table 6. Trip Modes intent files

File

Content

trip_notification.intent

start notification trip

trip_suggestion.intent

start suggestion trip

trip_action.intent

start action trip

Simulated trip’s sequence of actions for each

Trip Mode which takes into consideration

utterances, dialogs and simulated ECU’s

messages are shown in corresponding sequence

diagrams. On “Figure 4. Skill Telltales

Notification Mode sequence diagram”, after

welcome message is provided by the voice

communication module running inside an UX

ECU, utterance from user is received to start a

trip in Notification Mode, then simulated ECUs:

Seat Belt, Fuel Level and Battery Level provide

corresponding status to finally trigger

notification messages with predefined simple

sentence dialogs. Decision on what to do next

relies on the user.

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Figure 4. Skill Telltales Notification Mode sequence diagram.

On “Figure 5. Skill Telltales Suggestion Mode

sequence diagram”, after welcome message is

provided by the voice communication module

running inside an UX ECU, utterance from user

is received to start a trip in Suggestion Mode,

then simulated ECUs: Seat Belt, Fuel Level and

Battery Level provide corresponding status to

finally trigger suggestion messages with

predefined sentence dialogs based on context

information about the car’s location. Even

decision of what to do next still relies on user, on

this mode the decision is facilitated.

Figure 5. Skill Telltales Suggestion Mode sequence diagram.

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On “Figure 6. Skill Telltales Action Mode

sequence diagram”, after welcome message is

provided by the voice communication module

running inside an UX ECU, utterance from user

is received to start a trip in Action Mode, then

simulated ECUs: Seat Belt, Fuel Level and

Battery Level provide corresponding status to

finally trigger action messages with predefined

sentence dialogs based on context information

about the car’s location. Decision of what to do

next relies completely on assumed autonomous

car.

Figure 6. Skill Telltales Action Mode sequence diagram.

3.3 Skill Telltales class

The Mycroft generated and later customized

Skill Telltales python class, through the methods

handle_telltales, handle_notification,

handle_suggestion and handle_action is the

main responsible to process each telltale's status

request and speaks a predefined output according

to the dynamic behavior represented in “Figure

3. Trip Modes state chart”, “Figure 4. Skill

Telltales Notification Mode sequence diagram”,

“Figure 5. Skill Telltales Suggestion Mode

sequence diagram”, and “Figure 6. Skill Telltales

Action Mode sequence diagram”.

3.4 Test cases

To provide a real example of the Skill Telltales

class in action “Table 7. Test cases for Skill

Telltales” summarizes meaningful test cases, this

is the input called intent, utterance or command

provided by a human with his voice to Mycroft

AI and corresponding output called dialog

provided Mycroft AI to the human.

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Table 7. Test cases for Skill Telltales.

Intent (Human)

Dialog (Mycroft AI)

Car seat belt status

Current car seat belt is Unfastened

What is the car fuel level status

Fuel level status is Reserve

Give me the car battery level status

Car battery level status is Low

Start Notification Trip

Notification messages from simulated trip

in Notification Mode

Start Suggestion Trip

Suggestion messages from simulated trip

in Suggestion Mode

Start Action Trip

Action messages from simulated trip in

Action Mode

4. Results and discussions

The main goals and results achieved in this work

are listed as follows:

 Voice-Based Virtual Assistants

investigation result reveals an increasing trend in

the automotive industry, this is confirmed by

their omnipresence in current and short-term

future vehicles, also it is proof enough of their

relevance as a big value asset that must be paid

attention to.

 Contemporary Open-Source Virtual

Assistant solutions presented here as

investigation result, evidences the worldwide

motivations to work and contribute to the

improvement and refinement of methods,

algorithms and techniques related to the NLP.

 Mycroft AI demonstrated to be the most

complete Open-Source Virtual Assistant based

on the facts of installation and setup on a Linux

PC is quite simple, documentation and

troubleshooting is available and helpful, and

finally, deployment of voice applications or skills

is straightforward aided by the MSK.

 The skill Telltales created by following the

UCD approach and the Design Thinking method

over three telltales seat belt, fuel level and battery

level, represents a voice communication module

for automotive instrument panel indicators with

the following characteristics: 1) Simple, because

skill output folder and dependency files are

reduced and concrete, 2) Portable, since it can

be deployed in hardware like RPI, Android or

Linux PC which has Mycroft AI instantiated, and

3) Customizable, though the adaption of the

intent, dialog, entity files as well as the python

based class of the skill.

5. Conclusions

The main contribution of this work is to lay the

foundations of the evolution of contemporary

Automotive Instrument Panels from a mere

physical device to a comprehensive virtual

device, this is, automobile users demand of future

vehicles voice-based virtual assistance, which is

easily achieved by using Mycroft AI, besides the

unique footprint of Mycroft AI is the deployment

of the UCD approach and the Design Thinking

method that makes the difference against

competitors.

For this work, the scope of the Instrument Panel’s

indicators was reduced to only three telltales

which are safety relevant: seat belt, fuel level and

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battery level, however real Instrument Panel’s

indicators are numerous safety relevant or non-

safety relevant, a short list of intents and dialogs

was introduced in corresponding files to keep it

simple, and finally a fixed status for each telltale

was set in the corresponding python class to

conclude generic dialogs.

About the applicability of the contribution of this

work, the automotive industry whether big

companies, mid-range companies or start-ups are

the main interested parties and beneficiaries of

this work, since voice communication modules

are fully compatible with any kind of HMI

products inside the car. Due to the fact Mycroft

AI is an open-source solution, its usage brings

economic benefits along with legal

responsibilities.

Future work relies on the vast possibilities of

connection and expansion of the voice

communication module presented in this work,

through the usage of countless python libraries to

import any kind, format or source of information,

for example, different systems interconnection

through communication buses and technologies

already available in the automotive domain like

CAN, LIN, Ethernet, MOST, GPS location

between others, in order to get, process and

present relevant information to the final user.

6. Acknowledgment

To Posgrado CIATEQ A.C. due to the

institutional support and guidance received to

conclude this work in a professional and

successful way. To Continental Automotive

Occidente due to the sponsorship provided to

perform the master’s degree along with Posgrado

CIATEQ A.C. which made possible this work.

To Dr. Francisco Javier Ibarra Villegas due to

their guidance and support on the process to

shape and concrete this work.

7. Authorship acknowledgment

Ricardo Hernández Mejía: Conceptualization,

Methodology, Software, Validation, Formal

analysis, Research, Resources, Original draft,

Visualization, Project administration. Francisco

Javier Ibarra Villegas: Review and Editing,

Supervision, Project Administration. Cain Pérez

Wences: Review and Editing.

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