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Para os procedimentos de pesquisa apresentados, iremos utilizar os procedimentos:

Experimental: Onde realizaremos experimentos em relação ao protótipo para verificar o seu funcionamento nominal e realismo.

Bibliográfica: Pois precisamos de embasamento de pesquisas anteriores para provar a veracidade do uso de simuladores.

Descritiva: Tendo nosso objeto de estudo como o ensino da condução de automóveis, podemos descrever características de seu funcionamento e como ele pode ser alterado com a inclusão de simuladores em seu processo.

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O simulador possibilita o ensino de condução com mais eficiência, aumentando a qualidade dos CFCs no Brasil.

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ANDRIOLA, César Luís. Análise da validade comportamental de um simulador de direção imersivo. 2021.

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ANDRIOLA, César Luís. Análise da validade comportamental de um simulador de direção imersivo. 2021.

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Resumo: Esta pesquisa consiste na análise do uso de simuladores de condução no ensino, e busca garantir que eles representem de maneira efetiva a tarefa de direção, são coletados dados de direção em ambientes reais, assim como simulados. O desempenho de ambos é analisado, apontando como a imersão e realismo é um fator chave para a transferências de habilidades dum ambiente simulado para um real.

Relevância: Para nosso projeto, esta pesquisa delimita a eficiência e efetividade do uso de simuladores em treinos de pilotagem, os pontos fortes do meio de ensino e como eles podem ser implementados de maneira que complemente o ensino comum em um ambiente de auto escola.

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Análise da validade comportamental de um simulador de direção imersivo

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Simuladores de direção se apresentam como ferramentas de elevado potencial de pesquisa no que se refere ao estudo do comportamento humano durante a tarefa de dirigir um veículo, tendo em vista a importância desse na ocorrência de acidentes de trânsito. Todavia, é necessário garantir que os simuladores representem corretamente essa tarefa de direção, por meio do processo de validação dos mesmos, ainda incipiente na literatura. Nesse sentido, o objetivo do presente trabalho é avaliar a validade c…

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P4: Dificuldade no aprendizado da condução de automóveis

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P4: Dificuldade no aprendizado da condução de automóveis

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De acordo com a 'Flight School USA', em âmbitos como a aviação, a simulação serve como uma ferramenta crítica para a preparação de pilotos em diversas eventualidades. A situação é semelhante em automóveis terrestres, entretanto, muitas escolas de condução não utilizam simuladores por seu custo alto de compra e manutenção.

Com sua eficiência comprovada em outros ramos da pilotagem, escolhemos desenvolver um simulador de condução com custo acessível que emule situações reais em automóveis terrestres com mais de duas rodas.

A realização de um protótipo envolve a produção de um volante, pedais e manopla de câmbio que possam ser conectados em um computador para uso com um software de simulação de condução.

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QD: Motoristas em treinamento precisam de um simulador de condução com custo acessível para obter as habilidades de dirigir um automóvel num ambiente seguro.

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QD: Motoristas em treinamento precisam de um simulador de condução com custo acessível para obter as habilidades de dirigir um automóvel num ambiente seguro.

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Estrutura da QD: [Quem] precisa [Do quê] pois [Como isso impacta o quem?] ou para [Resolver qual problema?]

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Coordenador de Comunicação & Coordenador de Pesquisa

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Líder & Coordenador de Produto

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Relator & Coordenador de Qualidade

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P1: Falta de água em casas com alta elevação;

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P1: Falta de água em casas com alta elevação;

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P2: Congestionamento em ruas com hospitais, dificultando a entrada de ambulâncias e necessitados

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P2: Congestionamento em ruas com hospitais, dificultando a entrada de ambulâncias e necessitados

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P3: Infiltração sonora em espaços residenciais

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P3: Infiltração sonora em espaços residenciais

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P4: Dificuldade no aprendizado na condução de automóveis

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P4: Dificuldade no aprendizado na condução de automóveis

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P5: Falta de infraestrutura para evitar o calor em salas fechadas

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P5: Falta de infraestrutura para evitar o calor em salas fechadas

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P8: Equipamento laboratorial de má qualidade em cursos industriais

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P8: Equipamento laboratorial de má qualidade em cursos industriais

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P9: Bebedouros quebrados ou em falta em escolas e espaços profissionais

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P9: Bebedouros quebrados ou em falta em escolas e espaços profissionais

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P10: Preços altos e baixa quantidade de tênis que auxiliam em problemas ortopédicos

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P10: Preços altos e baixa quantidade de tênis que auxiliam em problemas ortopédicos

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Seção de Brainstorming realizada dia 14/03/2025

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Seção de Brainstorming realizada dia 14/03/2025

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QO: Quais os efeitos da aplicação de um simulador de direção realista que podem contribuir no aprendizado da condução de alunos de autoescola?

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QO: Quais os efeitos da aplicação de um simulador de direção realista que podem contribuir no aprendizado da condução de alunos de autoescola?

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Estrutura da QO: Articulador Qualificador + Articulador Solução + Objeto de estudo (Proposta/Tese) +

Articulador Ligação + Problema

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Peça central do volante, será programada por meio de um drive e microcontroladores para simular os movimentos de um volante.

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Lê e controla a posição do motor e seus movimentos.

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Por meio de um ímã fixado no raio, envia instruções de posicionamento para a placa controladora.

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(Exemplo de ímã utilizado na leitura pelo encoder)

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(Exemplo de ímã utilizado na leitura pelo encoder)

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Usada na alimentação de diferentes peças do projeto, como o O-Drive.

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Por emitirem sinais de potenciômetro, são compatíveis com a programação de drivers e microcontroladores, como as plataformas ESP32/Arduino

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Fixação de dois resistores de frenagem. Utilizados para dissipar a energia em calor e evitar sobrecargas.

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Primeiro protótipo do encapsulamento.

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Primeiros testes do volante com o encapsulamento.

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Motor fixado numa base com suportes SK e peças de apoio impressas em 3D.

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O-DRIVE Fixado numa base impressa em 3D com filamento PLA

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Visão lateral de um drive e resistores de frenagem fixos numa base.

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Conexão entre motor e base, por meio de um "Raio" unido a um cubo espaçador. Utiliza peças de apoio impressas em 3D e apoios de alumínio SK.

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Primeira versão do encapsulamento finalizada.

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Cabo Mike duas vias utilizado na preparação da fonte para alimentação.

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Fonte Chaveada com conector femea mike 2 vias, um conector para cabo de força tripolar com fusivel e botão liga e desliga.

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Disciplinas do curso M-TEC: Eletrônica que são relevantes para o desenvolvimento deste trabalho incluem:

Sistemas Embarcados I, II e III;

Desenho Informatizado em Eletrônica;

Projetos de Sistemas Eletrônicos.

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Título Provisório: "Aplicação de um simulador de condução de custo acessível para ensino da pilotagem de automóveis em autoescolas."

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Título Provisório: "Aplicação de um simulador de condução de custo acessível para ensino da pilotagem de automóveis em autoescolas."

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Estrutura do título provisório:

a) O problema ou desafio.

b) Objeto de estudo foco para a pesquisa

c) População ou contexto abrangido pelo estudo.

Limite: 20 palavras em 3 linhas, contendo os elementos que delimitam o tema de pesquisa.

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Pertinência & Relação com o perfil profissional

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Pertinência & Relação com o perfil profissional

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O objeto de estudo escolhido está alinhado com as seguintes competências do curso técnico em eletrônica:

Projeção de sistemas eletrônicos embarcados;

Aplicação de tecnologia de circuitos microprocessados e microcontrolados;

Supervisão e instalação de equipamentos eletrônicos;

Testes e calibração realizada com uso de instrumentos de medição;

Controle de qualidade;

Criação de relatórios de documentação técnica

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De acordo com dados do governo coletados pelo IDEC (Institudo de Defesa de Consumidores), o número de pessoas que possuem habilitação para dirigir no Brasil atingiu 74 milhões em 2020, categorizando-o como um documento fundamental para inúmeros brasileiros, e a aplicação de ferramentas de ensino, como um simulador de condução que facilite o aprendizado, seria relevante para todos que estão no processo de obtenção da CNH. (Carteira Nacional de Habilitação)

Fonte: IDEC - Número de CNHs cresce 38% em 10 anos, e cai proporção de jovens motoristas; link anexado

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Número de CNHs cresce 38% em 10 anos, e cai proporção de jovens motoristas

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Total de motoristas no Brasil chegou a 74 milhões neste ano

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Ao analisar o escopo de nosso projeto, levando em consideração nossos conhecimentos de eletrônica, e a DeadLine de 09/10/2025, avaliamos como plausível a realização do projeto com os recursos que adquirimos ao longo do desenvolvimento deste TCC. Temos acesso as ferramentas necessárias e um orçamento definido para a sua execução.

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De acordo com o IDEC (Instituto de Defesa dos Consumidores), a parcela de motoristas abaixo dos 30 anos foi de 29% em 2010 para 21% em 2020, o que indica uma diminuição no número de jovens adquirindo a CNH. Portanto, a criação de uma ferramenta de ensino que auxilie neste ensino impactaria de maneira positiva aqueles que passam pelo processo de habilitação no Brasil.

Fonte: IDEC - Número de CNHs cresce 38% em 10 anos, e cai proporção de jovens motoristas

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Número de CNHs cresce 38% em 10 anos, e cai proporção de jovens motoristas

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Total de motoristas no Brasil chegou a 74 milhões neste ano

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Após análise da perspectiva de nosso projeto e dos coorientadores disponíveis para realização do projeto, escolhemos prosseguir sem a participação de um coorientador.

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GLASSMAN, Jeffrey Edward. Young Driver Training and Socioeconomic Status: A Human-in-the-Loop Driving Simulator Evaluation. 2024. Dissertação de Mestrado. Old Dominion University.

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GLASSMAN, Jeffrey Edward. Young Driver Training and Socioeconomic Status: A Human-in-the-Loop Driving Simulator Evaluation. 2024. Dissertação de Mestrado. Old Dominion University.

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Resumo:Este estudo utiliza o simulador de condução como meio de avaliação para notar as diferenças de ensino entre pessoas com status socioeconômico diferente, além dos efeitos que isso causa em motoristas jovens em comparação com os mais experientes.

Relevância: Entender as barreiras que separam gerações diferentes e classes sociais distintas no aprendizado da condução serve um propósito para nosso projeto, que visa a acessibilidade financeira de ferramentas do ensino da direção.

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Young Driver Training and Socioeconomic Status: A Human-in-the-Loop Driving Simulator Evaluation

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Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.

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FEINAUER, Sophie et al. The potential of gamification for user education in partial and conditional driving automation: A driving simulator study. Transportation research part F: traffic psychology and behaviour, v. 90, p. 252-268, 2022.

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FEINAUER, Sophie et al. The potential of gamification for user education in partial and conditional driving automation: A driving simulator study. Transportation research part F: traffic psychology and behaviour, v. 90, p. 252-268, 2022.

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Resumo: O conceito de 'gamificação' em autoescolas é examinado neste estudo, e apresenta ser uma ferramenta que habilita o maior engajamento entre professor e aluno, algo que ocorre por meio de simuladores combinados com fundamentos da gamificação.

Relevância: Este estudo ajuda a demonstrar a imersão extra proporcionada por um simulador 'gamificado' num ambiente de ensino, mostrando o seu potencial como uma ferramenta que pode ser introduzida em escala maior para o aprendizado da condução de automóveis.

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FARIDIAGHDAM, Amin et al. Exploring the cognitive and behavioral factors impacting novice young drivers: Structural equation modeling of situational awareness, driving skills, reported crash history, and violations, using a driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour, v. 111, p. 130-144, 2025.

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FARIDIAGHDAM, Amin et al. Exploring the cognitive and behavioral factors impacting novice young drivers: Structural equation modeling of situational awareness, driving skills, reported crash history, and violations, using a driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour, v. 111, p. 130-144, 2025.

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Resumo: Com o foco em motoristas jovens, este estudo relata fatores cognitivos relacionados com acidentes e violações de leis de trânsito nesta faixa etária.

Relevância: O estudo nota como simuladores permitem encontrar mais facilmente pontos fracos e possíveis infrações cometidas por condutores em aprendizado, mostrando-o como uma ferramenta para correção de problemas e melhora do aluno como motorista.

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THORSLUND, Birgitta et al. Simulator-based driving test prescreening as a complement to driver testing–Toward safer and more risk-aware drivers. Accident Analysis & Prevention, v. 194, p. 107335, 2024.

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THORSLUND, Birgitta et al. Simulator-based driving test prescreening as a complement to driver testing–Toward safer and more risk-aware drivers. Accident Analysis & Prevention, v. 194, p. 107335, 2024.

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Resumo: Utiliza de simuladores como uma ferramenta para testar a identificação de ambientes e situações de risco na estrada, visando a atenção e facilidade de reconhecimento de problemas.

Relevância: Este estudo demonstra como a simulação não só pode ser utilizada como maneira de obter conhecimento sobre a pilotagem e funcionamento mecânico de um automóvel, como também auxilia com o aprendizado de fatores externos da estrada, como sinalizações e ambientes de perigo.

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KRASNIUK, Sarah et al. The effectiveness of driving simulator training on driving skills and safety in young novice drivers: A systematic review of interventions. Journal of Safety Research, v. 91, p. 20-37, 2024.

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KRASNIUK, Sarah et al. The effectiveness of driving simulator training on driving skills and safety in young novice drivers: A systematic review of interventions. Journal of Safety Research, v. 91, p. 20-37, 2024.

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Resumo: Compara as situações de estudo e demonstração incluídas num simulador com aquelas presentes no cotidiano, notando como é uma ferramenta mais favorável a descrever estas mesmas situações em livros e salas de aula.

Relevância: Este estudo demonstra como uma ferramenta "Hands-on", que permite o aluno enfrentar diversas condições em um ambiente controlado, serve como uma alternativa valiosa quando comparada com métodos de ensino tradicionais e que não envolvem participações do aluno, como explicações em sala de aula.

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ALONSO, Francisco et al. Effectiveness of driving simulators for drivers’ training: a systematic review. Applied Sciences, v. 13, n. 9, p. 5266, 2023.

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ALONSO, Francisco et al. Effectiveness of driving simulators for drivers’ training: a systematic review. Applied Sciences, v. 13, n. 9, p. 5266, 2023.

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Resumo: A implementação da tecnologia em mediar situações de risco na condução de um automóvel é observada neste estudo, concluindo que ferramentas como as de simulação demonstraram um valor empírico na compreensão de conceitos de segurança na estrada.

Relevância: As observações em relação aos processos de avaliação envolvendo a simulação mostra pontos de melhora técnica e ergonômica para este projeto, além de observar outras ferramentas de ensino que se beneficiam da colaboração com a simulação.

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Research Institute on Traffic and Road Safety (INTRAS), University of Valencia, 46022 Valencia, Spain

Research Institute on Robotics, Information and Communication Technologies (IRTIC), University of Valencia, 46980 Valencia, Spain

Author to whom correspondence should be addressed.

1. Introduction

1.1. Simulators in Context

Since their appearance in the 1940s, simulators have been gaining ground and consolidating themselves as "efficient" and risk-respectful tools for driver training under controlled risk conditions. In the beginning, given the large outlay involved, only the Administrations were able to commission the development of simulators, and the United States Army was one of the first sponsors.

In the 1960s and 1970s, with the advent of the first digital computers and computer graphics, the U.S. Army commissioned the development of the first "full mission" simulator in history [ 1]. It was an aircraft flight simulator for training pilots that reproduced both the cockpit and a virtual scenario in which the aircraft flew. It was qualified as "full mission" because it allowed pilots to be trained from takeoff to landing.

With the passing of the years and the great advances in the technological field (microprocessors, screens, projection, controllers, etc.), costs grew lower and, in addition to the Administrations, large companies began to develop their own simulators. This encouraged the appearance of simulators for all types of vehicles: flight [ 2], ship [ 3], submarine [ 4, 5], car [ 6, 7], truck [ 8, 9], etc. As an example of this expansion, in the 1970s, there had already been 28 driving simulators developed worldwide [ 10].

Initially, simulators were used only for training in areas or situations that were either too dangerous for users to practice in real life or were much more expensive to reproduce in real life than the investment needed to develop a simulator. Today, this concept has expanded, with entertainment being one of the main niches in the field of simulation. On the other hand, there are also simulators intended exclusively for scientific use.

1.2. Simulator Classification

Currently, there are so many companies dedicated to the design, development, and manufacture of driving simulators that there are very different criteria used to classify them [ 11]. Some of these criteria are:

According to their purpose [ 12], simulators can be classified into three groups: (i) training, when educational objectives are pursued or related to the prevention of risks and traffic accidents. These, in turn, can be divided into professional and amateur simulators. This is the group that we focus on in this study. Among other uses (outside the scope of this study), simulators are usually-albeit not only-applied for (ii) research (when the purpose of the simulator is to investigate a certain area of knowledge), and even (iii) entertainment (when the purpose of the simulator is to amuse and entertain).

Based on their physical characteristics, simulators are usually classified on the basis of their visualization system (e.g., field of view, projection system) [ 13]; pixel size resolution (i.e., better or worse feeling of user involvement and immersive experience) [ 13]; cockpit (having/not having a sensorized driving cab, which allows for driving as if in a real vehicle) [ 14]; sound system (e.g., two-way sound (stereo), surround systems, or "8.1" systems) [ 15]; force-feedback (small motors providing users with feedback) [ 16]; and motion platform (allowing the user to reproduce the accelerations that would be felt in the simulator) [ 17].

In addition, according to their software-related characteristics, the most common simulator uses are procedural simulation (used for formative/training purposes) [ 18]; full-mission (used for professional driving training) [ 19]; games (generally intended for entertainment) [ 20]; and configurable settings (situations can be live-edited) [ 18].

1.3. Simulators for Driving Training

This study covered the specific case of simulators intended to train divers, which has an extensive background principally grounded on the concept of edutainment. It can be understood as "the combination of education and entertainment in a learning process" [ 21, 22]. Buckingham and Scanlon [ 23] previously stated that "edutainment is based on attracting and maintaining the attention of learners by using screens or animations to make learning fun"; in other words, it is based on the use of simulators. In fact, it has been shown [ 24, 25, 26] that, thanks to the use of these new technologies, which often include various stimuli such as images, sounds, and videos, students are more likely to pay attention to the content and end up transferring it from short-term to long-term memory, thus becoming more entrenched in their knowledge. This is especially relevant in in-vehicle driving simulators, since, on the one hand, general skills are taught, and on the other hand, participants are trained in situations that rarely occur in real life are, but for which one must be prepared.

Driving simulators are so widely used today that in some countries, individuals are even required to pass certain tests on simulators in order to obtain a driver's license [ 27, 28]. These countries include the United Kingdom, the Netherlands, Singapore, and Finland [ 27, 29, 30]. One of the great advantages of using simulators is that they are able to measure, in an analytical way, everything that is happening with the simulated vehicle and whether the user is reacting correctly or not to the situations that arise. Another great advantage of simulators is that they allow the same situation to be recreated reliably over and over again (replicability), which makes it possible, on the one hand, to train complex situations without putting any user at risk, and, on the other hand, to make requirements for passing a test more objective, since all users can face exactly the same situation. In other countries, such as Saudi Arabia, where women were recently allowed to apply for driving licenses, a large number of requests for new driver training were received in a short period of time, and had it not been for the use of simulators, it probably would not have been possible to meet the high demand [ 31].

1.4. Theories of Performance and Implications for Simulation-Based Training (SBT) Measurement

Salas et al., 2009 [ 32], provided a review of the state of the science on simulation-based training (SBT) performance measurement systems. It states that training using traditional methods, such as lectures or conferences, is insufficient to meet the demands of many modern work environments or organizations. That is why they are resorting to the use of simulators to transfer this knowledge, which is usually very practical and oriented to situations that participants will encounter in the real world.

The effectiveness of all the aforementioned types of simulator requires a set of generic/standardized actions, such as a guided training plan and a continuous measurement of staff performance, so that their aptitude can be evaluated and the training can be fed back by these measurements. If a performance measurement is not correctly adjusted for individual or teamwork use, it will certainly lead to a waste of time and money for both the trainee and the company providing the training.

There is currently a wide variety of theories on how performance should be measured and its implications in SBT-based systems, for both individual and collective learning (teamwork).

For the measurement of individual performance, the work of Campbell et al. [ 33] stands out; they state that performance depends on three variables: declarative knowledge, i.e., the facts and knowledge necessary to complete a task (understanding the task requirements); procedural knowledge and skills, i.e., the combination of knowing what to do and how to do it correctly; and, finally, motivation, i.e., the combination of the expenditure, level, and persistence of effort required for learning.

Regarding the measurement of team performance, there is a wide discussion [ 34, 35] on the definitions of performance and its effectiveness. This has resulted in the formation of different frameworks depending on the specific learning context. Training a flight crew is not the same as training a marketing or human resources team. Among the different existing theoretical frameworks for measuring team performance in SBT are input-process-output (IPO) [ 36, 37, 38], shared mental models [ 39, 40, 41], adaptability [ 42, 43], the "big five" of teamwork [ 43, 44, 45, 46, 47], and macrocognition/team cognition [ 48].

These theories require certain methods for their application in measuring SBT performance and feedback. Salas classifies these methods according to whether they are qualitative or quantitative. Those of the former type are used to define the simulation system to be developed and the measurements to be made. These include protocol analysis [ 49], the critical incident technique [ 50], and conceptual maps [ 51]. The latter type consists of those which, based on the former, quantify the developed processes and provide feedback to the system to correct what is necessary. These include behaviorally anchored rating scales, or BARS [ 52]; behavioral observation scales, or BOS [ 53]; communication analyses [ 54]; event-based measurement, or EBAT [ 55]; structural behavioral assessment [ 39]; self-report measures; and, finally, automated performance recording and measurement [ 55].

Simulation is a field that is increasingly incorporated into our daily lives. In recent years, research has emerged with new methods to measure simulation-based learning. Papakostas et al. [ 56] proposed a novel and well-established model to measure the user experience and usability of a simulation-based training application. This method combines the perceived usefulness, the perceived ease of use, the behavioral intention to use the system, and two more external variables to conclude the user acceptance of the simulation system. Some years before, the same authors proposed a similar method based on the evaluation of external variables considered to be "strong predictors" [ 57].

As can be seen, the variability of measurement types for performance in simulation-based coaches is enormous. Therefore, this methodological review does not focused on any of them, but rather presents the different studies published to date, exposing the results obtained and the methods used for each of them. INTRAS (Institute of Traffic and Road Safety) and IRTIC (Institute of Robotics and Information and Communication Technologies) of the University of Valencia have been working together for many years in the area of driving simulation. They have developed dozens of simulators and campaigns aimed at both professional and novice drivers, always attempting to objectify the results each simulator transmits to its users [ 58, 59]. Similarly, the scientific community has spent many years studying the impact of each type of simulator on each type of training, and this has been the source of a large number of publications, some of them contradicting the results of others and questioning the effectiveness of certain simulators [ 14].

Therefore, it is considered necessary to systematically review what has been published to date regarding the effectiveness of simulation systems for driver training. The following sections cover this need.

1.5. Study Aim

Considering the aforementioned issues, the core aim of this systematic review was to analyze the existing original literature on driving simulators as tools for driver training/instruction. We considered the features of the studies, their quality, and the established degree of effectiveness of the simulators for these purposes.

2. Materials and Methods

In the development of the systematic review, five steps were followed ( Figure 1).

2.1. Step 1: Identifying the Research Question

The objective of the present systematic review was to identify scientific articles that have evaluated the effectiveness of driving simulators as tools for driver training and education. Therefore, articles that used driving simulators for other research purposes were discarded. Studies that analyzed drivers' performance, the presence of certain variables specific to the driving task, or any other type of study whose objective was not the use of driving simulators in a training context were not included.

Therefore, as for our purposes, we sought to determine the degree of effectiveness of driving simulators for this purpose, as well as to collect and record the main findings evidenced by the scientific literature and to discover the limitations that may have existed in the evaluation process. No comparisons were made. The results include a summary and a thematic analysis of all selected articles.

2.2. Step 2: Finding Relevant Studies

Initially, a scoping review of the literature was carried out to preliminarily evaluate the potential and scope of the research objectives, as well as to identify the key terms to be applied in the search strategies for the subsequent stages of the review process.

Once the scoping review had been carried out, four databases were selected to develop the search strategy for articles: Web of Science, Scopus, PubMed, and Cochrane Library. These bibliographic databases were chosen because of their recognition as reliable indicators of quality valued by the scientific community and, especially, by the field of research in which the thematic area of the review is framed. In addition, we searched other reference lists of various reviews of potentially eligible scopes that did not appear when the search strategies were applied.

The search was conducted in the second week of August 2022. There were no exclusion criteria related to the year of publication. Therefore, for the present review, all the literature published from the beginning of the database to the date of the search was considered as potentially eligible before applying the systematic review filters.

In addition, the search strategy was carried out taking into account that the review covered research published in both English and Spanish. Therefore, we used the same Boolean search operator in all the databases: "(evaluation OR evaluación OR effectiveness OR efficiency OR efectividad) AND (driving AND conducción OR simulador AND conducción) AND (training OR education OR formation OR research OR entrenamiento OR formación OR educación)". Both the keywords for the search and the Boolean operator were agreed upon by the authors after the scoping review was performed.

2.3. Step 3: Selecting the Studies

During this step, articles that did not specifically address the research objective were excluded. All authors initially and independently evaluated the manuscripts and subsequently met to discuss and resolve any discrepancies.

Only scientific articles were included, avoiding the inclusion of gray literature. Therefore, publications in the form of letters, doctoral dissertations, conferences/abstracts, editorials, case reports, protocols, or case series were not selected. Eligibility criteria were also restricted to articles for which the full text was accessible, either because they were available due to their open-access status or because they could be requested through the library system.

2.4. Step 4: Charting and Collating the Data

Articles that met the inclusion criteria were critically reviewed using the descriptive-analytic method of Arksey and O'Malley (2005) [ 63]. The extraction was recorded for each eligible article, including author(s), year of publication, country of study, objectives, method and sample, results (main outcomes), and key limitations.

2.5. Step 5: Summarizing and Reporting the Results

The data extraction was recorded in tabular form. The main findings were summarized and discussed, and the salient features of the selected articles were described. In addition, a quality assessment of the studies was performed to ensure that the results would not be significantly altered.

3. Results

3.1. Search Results

Figure 2 shows the process of searching and selecting data sources. Initially, the search strategy yielded a total of 1757 articles for analysis. After discarding all duplicate documents and ineligible articles for various reasons, 1229 were excluded. After reading the title, abstract, and full text, the articles were screened for whether or not they responded specifically to the objectives of the review and/or for whether or not they had sufficient quality indexes to be included, according to the criteria established by the Critical Appraisal Skills Programme (CASP). After this process, 15 eligible articles were obtained and included in the study.

3.2. Characteristics of the Eligible Research Articles

Although the search was conducted for articles published in both Spanish and English, the 15 eligible articles were published in English. The selected papers were published between 2003 and 2021, which is in accordance with the recentness and novelty of the subject matter of the study.

In addition, there were key geographical clusters grouping most of the research, defined in this study on a country basis. The studies were conducted in multiple geographical regions. In this sense, there was representation from eight countries located on three continents. Specifically, the selected studies were distributed among the United States (n = 6), France (n = 3), Italy (n = 1), United Kingdom (n = 1), Canada (n = 1), Switzerland (n = 1), the Netherlands (n = 1), and Korea (n = 1).

After assessing the studies selected for the analysis phase of this systematic review, the studies were classified according to their core features, e.g., year of publication, objectives, methodological approach, main outcomes, and key acknowledged limitations, if applicable. These data were tabulated in order to determine their interpretability and comparability, as suggested by the PRISMA guidelines. Table 1 shows the general characteristics of the analyzed original research articles.

Thus, in relation to the methodologies used in the research on the effectiveness of driving simulators for driver training, several elements of interest can be observed. On the one hand, most of the studies employed an experimental methodology. Therefore, in 7 of the selected studies, which represented 46.7%, groups of participants who had been trained with a training program using a simulator were compared with the control groups, consisting of participants who had not been subjected to this training process, to determine the effectiveness of the simulator for this task. In other cases, different training programs were compared, either with and without simulators or with various types of simulators with different degrees of realism for the user, but, in all cases, without using any group as a control (n = 4, representing 26.6% of the studies). On the other hand, only one study (6.7%) used the same simulator training method for different groups of users to analyze the differences depending on their roles as drivers [ 64].

The rest of the investigations (n = 5, representing 33.3%) employed a single group of subjects, so these were longitudinal studies in which intrasubject comparisons were performed, i.e., each subject was assigned all the conditions of the independent variable. Therefore, the magnitude of the disturbing variables was constant for each subject, and the variation observed in each subject was explained by the variation of the independent variable, which, in this case, was training or training with a driving simulator. Thus, the process was not identical in all studies of this type. In 2 studies (13.3% of the total number of studies selected), the same group was exposed to real road circumstances and to a simulator. In the other 3 studies (20.0%), the subjects were only evaluated before and after simulator training using this tool, without evaluating performance in real environments.

Regarding the results of the studies on the effectiveness of driving simulators for driver training, all but one of the studies observed an improvement in performance (n = 14, representing 93.3%). However, the effects were not equally strong in all studies. Thus, in 11 of them (73.3%), the improvements were significant in comparison with the subjects before receiving training or with the control groups that did not receive training. On the other hand, 4 studies (26.6%) observed that all the training programs they applied were beneficial for the improvement of the participants' performance, regardless of whether or not a simulator was used and the degree of realism of the tool. In any case, 2 of them (11.7%) emphasized that using more realistic simulators positively affected driver performance compared to other training programs. In addition, there were 2 studies (13.3%) that pointed out the partial benefits of simulator training, as they observed improvements in some driving tasks, such as reduction in errors and violations, but no significant improvements in other specific actions [ 73]. Finally, only one study (6.7%) did not obtain evidence of the effectiveness of simulators in improving the execution of driving tasks. However, it did observe learning in the evaluated participants [ 63].

3.3. Evaluation of the Quality of the Selected Studies

The Critical Appraisal Skills Programme (CASP) quality assessment tool was applied to ensure that no selected study could interfere with or distort the conclusions of this systematic review. This instrument allows for evaluation of the rigor and relevance of a scientific publication by means of ten specific questions [ 79]. The results obtained from the evaluation of the selected articles are shown in Figure 3. They all present a low risk of bias and have, therefore, been included in this review. In this process, four articles selected in the screening process were eliminated, as shown in Figure 2.

4. Discussion

In this sense, analyzing the effectiveness of simulators is important to determine the degree to which they are useful for improving driver performance [ 83]. The selected studies employed different methodologies in which groups that had received training with simulators and control groups that had not received it were compared, or the performance of subjects before and after receiving these training programs was compared [ 71, 75]. In practically all cases (exception McDonald et al. (2015) [ 63]), a significant improvement in the performance of the participants who received this type of training was evident. Thus, it is indicated that all types of drivers can benefit from simulator training, since it allows them to experience complex situations in a safe and controlled environment.

Specifically, several studies indicated that these training programs were particularly useful for novice drivers [ 64, 66, 77, 78] and older drivers [ 69]. These results contrast with other research conducted in these age groups, which has not presented evidence supporting the effectiveness of simulators for driver training [ 84]. For example, Hirsch and Bellavance (2017) [ 85] did not observe differences in the accident rate between drivers who received simulator training and those who did not. For their part, Rosenbloom and Eldror (2014) [ 86] also found no significant differences between novice drivers who had received a training program and those who had not. In any case, these studies were discarded from the article selection process because they presented important limitations that restricted the quality of the manuscripts. These limitations were mainly related to sample selection biases, biases linked to the unblinded self-assessment of the data, and the small sample size, factors which should, therefore, be partially taken into account when drawing conclusions.

In any case, the systematic review carried out herein did include research with homogeneous results that demonstrated the effectiveness of simulators for driver training. However, it should be noted that some variables may have interfered with learning or favored learning through simulators. According to the analyzed studies, one of the most relevant of these was the degree of realism of the utilized simulator. Allen et al. (2007) [ 76] compared three groups of participants trained with different simulators, from a very basic one with a narrow field of vision to a complete one consisting of a combination of wide-angle projected screens. They reported that the best results were obtained when the simulator was adjusted to real conditions. Madigan and Romano (2020) [ 72] shared these findings, observing that the group of participants trained on the simulator with the best video and projection quality obtained the best scores in the subsequent tests.

In this sense, the level of realism may influence whether the assessment of performance through the simulator is (or is not) different from the assessment of drivers in a real environment. In fact, van Paridon et al. (2021) [ 87] stated that the cyclists who participated in their research had a better perception of road objects in the simulator than on the road. They attributed these differences to the fact that the virtual environment had fewer elements than the real environment, making it easier for the subjects to focus their attention on the indicated objects since there were fewer distracting elements. On the other hand, other research demonstrated that the workload reported by the participants in the simulators was higher than that which they had on the road. Further, Lobjois et al. (2021) [ 67] performed physiological measurements in which they observed a higher flicker frequency in virtual environments. In addition, participants self-reported more stress in the simulator compared to real driving. In both cases, the differences occurred because the subject was not fully immersed in a realistic environment, but was aware that he/she was in a simulator, which may have altered performance levels in some cases.

Consequently, and given the potential differences in performance in both environments, it would be beneficial to assess drivers in real-world conditions after their training in virtual environments and/or driving simulators. Otherwise, regardless of a simulator being used (or not), driving training's typical setting makes it a road performance intervention; for this reason, some studies argue that even more reliable and valid assessment results might be obtained if an adequate balance between intra- and extra-simulator conditions were achieved [ 32, 55, 70].

Another variable that influences performance is the prevalence of certain driving situations or actions in the training period. Divekar et al. (2013) [ 70] note that, although driver training was effective for the trained tasks, it did not generalize to other driving actions. Therefore, to increase the effectiveness, realistic simulator conditions are not sufficient, but must be supplemented with road situations that trigger multiple driving tasks to ensure the most complete training possible. However, if systematic errors are observed with certain specific actions, the tool can also be adapted to repeat that action. Thus, performance would be improved in those tasks in which drivers perform more infractions, and would attend to those actions in which participants already show the correct performance to a lesser extent.

Additionally, the level of acceptability and confidence of the participants and their predisposition to use driving simulators can also influence the research results. Thus, Salvati et al. (2020) [ 88] pointed out that some participants may have limitations or difficulties in understanding the simulator system, influencing their performance and ability to take advantage of the tool. In addition, this level of confidence is variable and may increase through practice, or, on the contrary, may decrease if critical situations are experienced.

These results are congruent with other research in which the perceived degree of security these systems and the intention to use them are important variables for trust in technology, especially in a traffic context [ 89]. These are factors that can be influenced by the social and cultural context of the region in which these types of training programs are implemented. In addition, other studies recommend considering public acceptance as a relevant matter to strengthen alongside large-scale technological improvements, such as automated vehicles, advanced driving assistance systems (ADAS), and training driving simulators, given their ability to mark new technological trends with potential to increase drivers' safety and welfare [ 57, 90, 91].

Limitations of the Systematic Review

This systematic review was carried out following the PRISMA procedure in order to avoid possible biases in the data selection and/or recording. In addition, the inclusion/exclusion criterion was that the eligible articles were part of databases relevant to the scientific community; this was decided in order to guarantee an adequate selection of articles. Nevertheless, the present systematic review is not free of the limitations inherent to this type of study. Thus, the review may present publication bias, which occurs when research with "negative" or non-significant results is either published in lower-impact journals or not published at all. Consequently, such articles may not have been included in the review because they do not appear in high-impact databases [ 92].

This bias could be related to the final low number of research papers that met the established eligibility criteria. Thus, this circumstance could limit the breadth and scope of the research findings. In addition, it is possible that the non-indexed literature (although it may have methodological limitations or limitations in terms of the quality of the results) could provide more interesting information on this subject.

5. Conclusions

The results of this systematic review point to the problematic scarcity of literature on the formal evaluation of driving simulators for driver training, given the (overall) reduced number of high-quality studies meeting the PRISMA-based inclusion criteria. This means that, aside from the large number of existing simulation tools, there is limited information on the actual effectiveness of these tools for training purposes. In addition, although most studies demonstrated good quality, many of them showed external validity issues as a consequence of typical shortcomings such as limited research samples and the absence of follow-up procedures to determine the training outcomes. Nevertheless, most sources assessing the issue provided good insights into the value of driving simulators in improving human-based road safety under risk control settings.

To fill these gaps, some of the analyzed sources suggested strengthening the current evaluation processes, aiming to target the degree of contextual effectiveness of various simulators and, if necessary, being able to apply further modifications and ergonomic improvements to increase their training value for both novice and current drivers.

Moreover, as evaluations become more common and frequent in this area, there will also be further technical improvements and ergonomic adaptations of simulators to allow for their more frequent use in driver training. Therefore, the knowledge provided by the existing evaluations included in this systematic review, as well as those to be carried out in the coming years, will provide data of interest for the design and development of specific training programs using simulators for driver instruction.

Author Contributions

Conceptualization, F.A. and S.A.U.; methodology, S.A.U. and M.F.; software, M.F.-M.; validation, F.A., J.V.R. and M.F.-M.; formal analysis, S.A.U. and M.F.; investigation, S.A.U. and M.F.; data curation, F.A., J.V.R. and M.F.; writing-original draft preparation, M.F. and S.A.U.; writing-review and editing, S.A.U. and M.F.; visualization, F.A. and M.F.-M.; funding acquisition, M.F.; supervision, S.A.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partly supported by the research grant ACIF/2020/035 offered by the "Generalitat Valenciana" (Regional Government of the Valencian Community, Spain). The sponsoring body did not interfere with the study design; with the collection, analysis, or interpretation of the data; nor with the writing of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Research Institute on Traffic and Road Safety at the University of Valencia (IRB approval number: RE0002260522, from 26 May 2022).

Informed Consent Statement

Data Availability Statement

The systematic review does not require the creation of new data, the selected articles are available in open access in the databases used (Web of Science, Scopus, PubMed, and Cochrane Library).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Data flow chart (DFC) describing the methodological steps of the systematic review.

Figure 2. PRISMA diagram for this systematic review.

Table 1. Records of the general characteristics of the chosen studies.

Author/s, Year, and Country

Objectives

Methods and Sample

Results (Main Outcomes)

Key Limitations

To evaluate driving risk behaviors with the introduction of substitute safety measures (SSM) in simulator training.

The participants were 21 novice drivers, 16 senior drivers, and 21 commercial drivers. All underwent a simulator training program using SSM, and the results in terms of reducing risk behaviors were evaluated.

Novice drivers reduced all risky behaviors except for lane changing.
Older drivers reduced only speeding behavior.
Commercial drivers reduced all risk behaviors.
Simulator training with SSM is effective for traffic management strategies and for reducing risky driving behaviors.

Only simulator driving was evaluated, so the effect on the reduction in risk behaviors in real driving is unknown.

Lobjois et al. (2021) [ 67]
France

To examine the differences between driving behavior and level of mental workload, manifested in a (low-cost) simulator and actual driving.

The participants were 21 drivers who drove on a real route and an identical virtual route on which they were free to drive.

The driving speed in simulated conditions resembled that of real driving.
The workload level was higher in the simulator (assessed by flicker frequency, response time, and subjective ratings).

Lateral control measurements were not collected.
Results may differ with a high-fidelity driving simulator.

McDonald et al. (2015) [ 68]
United States

To determine whether RAPT-3 training would improve risky turning behaviors at intersections when no latent hazards were present.

Sample drivers under 18 years of age who had possessed a license for more than 180 days were assigned to either an intervention group that received RAPT-3 training (n = 18) or a control group that did not receive training (n = 19).

The group that received training scored higher than at the baseline assessment.
There were no differences between trained and untrained adolescents in traffic control errors and gap or collision selection.
Although the experimental group manifested learning, no differences in performance were observed at signal-controlled intersections in left turns where hazards were not latent.

Actual driving was not evaluated,
there was little variability in the simulated scenarios, and the
sample was limitated.

Divekar et al. (2013) [ 70]
United States

To evaluate the effectiveness of the FOCAL program, which teaches novice drivers to limit the duration of in-vehicle glances while performing a task.

The 20 participants were randomly assigned to the FOCAL or control group.
The eyes of the participants were monitored throughout the entire course.

The FOCAL-trained group performed better than the control group in the simulator task.
The training did not generalize to other actions, such as looking away from the roadway.

There was no information on participants' educational level or previous accidents.
The convenience sample may not have been representative of the novice driver population.
Actual driving was not assessed.

Roenker et al. (2003) [ 75]
United States

To evaluate the effects of a training program for the improvement of driving performance in older adults.

The participants were divided into three groups: (a) processing speed training (n = 48), (b) a traditional training program with simulator (n = 22), or (c) control group (n = 25). The sample was evaluated with a simulator and in a real driving context before training, after training, and 18 months later.

The simulator-trained group improved in the tasks of changing to the correct lane and using the signal correctly.
The other groups did not show significant improvements.

Methodological limitations

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# data[].attributes.attachment_link.title
Effectiveness of Driving Simulators for Drivers' Training: A Systematic Review

# data[].attributes.attachment_link.description
Although driving simulators could be commonly assumed as very useful technological resources for both novel and experienced drivers' instruction under risk control settings, the evidence addressing their actual effectiveness seems substantially limited. Therefore, this study aimed to analyze the existing original literature on driving simulators as a tool for driver training/instruction, considering study features, their quality, and the established degree of effectiveness of simulators for these purposes.

# data[].attributes.headline
ZHANG, Yanning; GUO, Zhongyin; SUN, Zhi. Driving simulator validity of driving behavior in work zones. Journal of advanced transportation, v. 2020, n. 1, p. 4629132, 2020.

# data[].attributes.subject
ZHANG, Yanning; GUO, Zhongyin; SUN, Zhi. Driving simulator validity of driving behavior in work zones. Journal of advanced transportation, v. 2020, n. 1, p. 4629132, 2020.

# data[].attributes.body
Resumo: O estudo busca comprovar o uso de simuladores para ver o comportamento em canteiros de obra como velocidade média, distância entre carros, e tempos de reação ao comparar com dados da vida real.

Relevância: O uso de um simulador proporciona um ambiente controlado, seguro e preciso, propício para a coleta de dados, estudos e correção de erros no trânsito e motoristas.

# data[].attributes.headline
LUIZ, Aline Santiago. Atenção, interações com smartphones e sua expressão em simulador de direção. 2021.

# data[].attributes.subject
LUIZ, Aline Santiago. Atenção, interações com smartphones e sua expressão em simulador de direção. 2021.

# data[].attributes.body
Resumo: Por meio de um simulador, o estudo relata as interações do motorista com objetos externos, como o smartphone, e como elas afetam a atenção na estrada e condução.

Relevância: Neste caso, o uso de simuladores permite avaliar a eficácia e a atenção do condutor, além de exemplificar para um possível aluno de auto escola, de maneira prática, como desviar a sua atenção durante a condução pode acarretar em resultados negativos.

# data[].attributes.headline
MACHADO, Lucas Cavalcante et al. Comparação da sensação de presença entre simulador de direção imersivo e não imersivo. In: Congresso de Pesquisa e Ensino em Transporte da ANPET (36.: 2022: Fortaleza). Anais [recurso eletrônico]. Rio de Janeiro: ANPET, 2022. 2022.

# data[].attributes.subject
MACHADO, Lucas Cavalcante et al. Comparação da sensação de presença entre simulador de direção imersivo e não imersivo. In: Congresso de Pesquisa e Ensino em Transporte da ANPET (36.: 2022: Fortaleza). Anais [recurso eletrônico]. Rio de Janeiro: ANPET, 2022. 2022.

# data[].attributes.body
Resumo: A pesquisa busca comparar modelos imersivos de simuladores de condução (com realidade aumentada e virtual), com simuladores não imersivos (compostos por monitores de computador).

Relevância: Este artigo nos proporciona motivos e argumentos para a escolha do tipo de imersão para nosso projeto. Para evitar o mal estar, ou "simulator sickness" que pode ser causado por uma imersão em realidade virtual, podemos utilizar uma estrutura não imersiva, composta de monitores comuns.

# data[].attributes.headline
ALLEN, R. Wade et al. The effect of driving simulator fidelity on training effectiveness. DSC 2007 North America, p. 1-15, 2007.

# data[].attributes.subject
ALLEN, R. Wade et al. The effect of driving simulator fidelity on training effectiveness. DSC 2007 North America, p. 1-15, 2007.

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Resumo: Um estudo sobre a fidelidade dos softwares de simulação em comparação com ambientes reais, observando campo de visão, gráficos, perspectiva e outros fatores.

Relevância: Para nosso projeto, este estudo demonstra a importância de configurações adequadas em FOV (Field Of View) para a sensação de realismo durante a simulação, as observações feitas sobre monitores, resoluções, tamanhos e quantidades também é proveitosa.

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Desenvolver um simulador de condução de custo acessível para auxiliar novos motoristas no aprendizado.

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Diante dos métodos apresentados, o tipo de pesquisa que se adequa ao objetivo de nosso projeto é o método de engenharia, pois visamos resolver um problema por meio de um produto que será prototipado.

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Experimentação: Pretendemos desenvolver uma hipótese sobre a aplicação de um simulador em ambiente de auto escola e como ele pode trazer benefícios para o ensino.

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Até o Deadline estabelecido, pretendemos apresentar um protótipo de um simulador de condução de automóveis desenvolvido de acordo com os nossos conhecimentos adquiridos durante o curso de Eletrônica.

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Desenvolver um protótipo de simulador que replica a experiência de dirigir um automóvel real, com o ensino em mente.

Testar o protótipo em condições semelhantes as reais, com componentes semelhantes aos de um carro, como volante, pedais e câmbio.

Empregar o simulador no processo de aprendizado, observando sua eficácia e realismo em relação às condições reais.

Identificar as limitações do simulador e propor possíveis melhorias com base nos resultados e recursos disponíveis.

Realizar um estudo sobre viabilidade econômica quando comparado a outros simuladores disponíveis no mercado.

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O processo de aprendizado ainda muito focado em teoria, baseado em experiência pessoal de um dos integrantes do grupo motivou o desenvolvimento deste projeto, dada sua relevância a outros estudantes de condução, considerando que acidentes no trânsito é uma das maiores causas de morte no Brasil, portanto, a criação de um ambiente onde se pode experienciar tais ocorridos em segurança e sem nenhum dano físico, mental ou psíquico reduzirá as fatalidades relacionadas a automóveis e motocicletas, como também contribuir para o desenvolvimento das ODS 4(Ensino de Qualidade) e 10(Redução de Desigualdades) ao deixar o ensino de condução mais acessível e eficiente. A aplicação deste estudo pode contribuir para o avanço de sistemas de simuladores no Brasil, fornecendo dados para futuras pesquisas.

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Nosso cronograma foi dividido em: Levantamento de Fontes e Bibliografia;

Confecção do Volante;

Confecção dos Pedais;

Confecção do Câmbio;

Resultados Finais.

As datas de início e conclusão, atribuições e progresso também estão incluídas no arquivo para cada passo da realização do projeto. O cronograma será atualizado de acordo com a progressão do trabalho.

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Vista superior do encapsulamento do motor em sua versão final, modelado e impresso em 3D com o material PETG.

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Visão interna do motor.

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Visão lateral do encapsulamento quando aberto, mostrando a conexão da O-Drive com o motor, fixado com o uso de suportes SK16.

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Caixa de Pedais, feito em madeira.

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Visão traseira dos pedais em sua versão final.

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Case do Arduino responsável por traduzir as posições do câmbio para o computador.

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Volante fixado na mesa.

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Visão frontal do projeto, fixado na mesa com câmbio adaptado, volante e pedais.

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Imagem do projeto em uso.

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Fotos do projeto sendo apresentado na feira tecnológica

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Fotos do projeto sendo apresentado na feira tecnológica

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Case do Arduino responsável por traduzir as posições dos pedais para o computador.

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TCC 2025 - Eletrônica - Simulador de Condução de Custo Acessível

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Ao reduzir os custos de um simulador, é possível implementar novos métodos de ensino com um menor custo monetário, tornando mais acessível para mais pessoas o aprendizado de condução.