Introduction

The increasing demand for frequent, driverless travel, coupled with rapid technological advancements, has accelerated the development of vehicle automation across Europe. Connected, Cooperative, and Automated Mobility (CCAM) technologies, including autonomous driving and connected mobility, present significant opportunities for addressing sustainability challenges in transport systems, particularly in urban and metropolitan areas1. The European Union (EU) anticipates a transition from Conventional Vehicles (CVs) to Autonomous Vehicles (AVs) over the coming decades. However, several critical challenges, including unresolved legal frameworks, road infrastructure limitations, and data privacy concerns, must be addressed to enable widespread adoption2. The societal, legislative, technological, and economic implications of AV adoption require careful consideration to ensure a balanced and effective transition.

In today’s rapidly evolving society, mobility plays a critical role in shaping individual well-being, driving economic progress, and fostering overall societal advancement. The decisions individuals make regarding transportation are influenced by various factors, including travel distance, time constraints, safety concerns, costs, comfort, and environmental considerations. These choices are further shaped by vehicle characteristics and the specific purposes of trips, highlighting the pressing need for mobility solutions that are not only efficient and reliable but also sustainable. Transportation research has undergone a significant transformation since the 1960s, with an increasing focus on comprehensive analyses that consider economic, policy, and infrastructure aspects3. The growing demand for faster and more frequent travel, coupled with ongoing technological innovations, has driven advancements in vehicle design and the automation of transportation, particularly within Europe. The EU envisions a future where AVs gradually replace conventional vehicles, a shift expected to unfold over the coming decades4. Autonomous driving is anticipated to provide numerous benefits, including increased convenience, enhanced safety, and greater efficiency. These advantages are widely documented in the literature, where AVs are expected to reduce crash rates, optimize traffic flow, and improve mobility for underserved populations5,6,7. However, the realization of these benefits is closely linked to public acceptance, user preferences, and the design of deployment strategies.

A growing body of research has explored individuals’ preferences for AV-use across various travel contexts, particularly in relation to multimodal and short-distance trips. AVs are often considered particularly suitable for first- and last-mile transport, especially when integrated with public transport systems. Yap et al. (2016)8 examined AVs as egress modes for train journeys and found that travelers, especially first-class train users, showed a preference for AVs over conventional modes such as bicycles or local transit. The study emphasized the significance of psychological factors such as trust and perceived sustainability, suggesting that user attitudes can substantially affect AV acceptance, beyond traditional factors like travel time and cost.

Similarly, Öztürker et al. (2022)9 conducted a stated choice experiment in the Netherlands to evaluate preferences for automated minibus services. Their findings indicated that service design, such as fixed versus flexible routes, affects preferences, with current public transport users favoring flexible, on-demand options. Moreover, attitudes toward technology, including trust, enjoyment, and perceived usefulness, were found to be key determinants of acceptance across user segments, particularly among those accustomed to private vehicles or active travel modes.

Another important aspect influencing AV attractiveness is the changing perception of travel time when passengers are freed from the task of driving. De Almeida Correia et al. (2019)10 demonstrated that the value of travel time (VOTT) can decrease in AVs, particularly when users can engage in work-related or leisure activities enroute. Their study revealed that while AVs with an office-like interior led to a lower VOTT compared to conventional cars, leisure-focused AVs did not significantly change time valuation. These results suggest that productivity during travel plays a key role in shaping users’ perceived utility of AVs.

Despite these promising prospects, potential negative externalities must also be considered. The increased convenience of AVs could lead to longer and more frequent trips, potentially resulting in higher vehicle kilometers traveled, increased energy consumption, and greater congestion or urban sprawl11,12. These unintended consequences underscore the need for thoughtful policy interventions aimed at managing AV adoption in a sustainable manner. Effective governance should not only support technological innovation but also mitigate risks by aligning AV deployment with environmental goals and urban planning strategies. Eventually, the widespread adoption of AVs will depend on the interaction between technological reliability, economic feasibility, and public trust. Addressing user concerns and aligning service design with evolving mobility needs will be essential to realizing the transformative potential of autonomous transportation.

Autonomous vehicles are generally defined as those capable of performing acceleration, deceleration, and steering functions without direct human intervention, relying on the vehicle’s ability to process environmental data13,14. The Society of Automotive Engineers (SAE) has classified automated vehicles into six progressive levels based on their degree of automation: Level 0 (No automation), Level 1 (Driver assistance), Level 2 (Partial automation), Level 3 (Conditional automation), Level 4 (High automation), and Level 5 (Full automation)15,16. Levels 0 through 2 involve varying degrees of human intervention, while Levels 3 through 5 represent increasing levels of automation, ending with complete independence17. To date, regulatory and legislative frameworks in EU countries are adapted according to this classification, underscoring the need for a specially designed legal framework that includes amendments related to autonomous vehicle technology at a pan-European and global level. Due to the rapid development of this field, it is imperative to create a flexible and adaptable framework that can address emerging challenges.

In addition to the legal and technological advancements, privacy and cybersecurity have become increasingly important as AVs rely on complex communication systems (V2X communications) and vast amounts of data. The integration of AVs into modern transport systems raises concerns about data privacy, as vast quantities of personal data are collected and processed by these vehicles18. Security vulnerabilities such as spoofing, timing attacks, and data interception highlight the need for robust cybersecurity measures to protect users and maintain public trust18. The EU’s focus on data protection, as outlined by the General Data Protection Regulation (GDPR), is crucial in maintaining confidence within AV technologies. These concerns, however, must be comprehensively addressed to ensure widespread acceptance and safe deployment of AVs.

Autonomous driving promises to offer numerous advantages, including improved safety by reducing human error19, alleviated driver stress as the vehicle handles transportation tasks20, enhanced parking availability, and better living conditions by reducing commuting time5. Additionally, autonomous driving promotes the adoption of electric vehicles, contributing to reductions in carbon emissions, as concluded by the University of Kentucky’s SWOT analysis21 (Fig. 1). Despite these advantages, several challenges accompany the integration of AVs, such as uncertainty regarding infrastructure, legal frameworks, and privacy concerns. The reliance of AVs on complex algorithms further raises concerns about their ability to make effective decisions in extreme or unpredictable scenarios, such as deciding whom to prioritize in an unavoidable accident5. These concerns highlight the urgency for a new legal framework to address responsibilities in the event of an accident, as many countries currently lack appropriate legislation. This legal uncertainty complicates liability, especially when determining fault in incidents involving AVs, as manufacturers may not always be held accountable for defects that emerge after a vehicle’s production20. In addition to legal challenges, economic barriers such as high costs, shifts in employment, and the potential rise of ridesharing models complicate the widespread adoption of AVs21.

Fig. 1: SWOT analysis for Autonomous Vehicles (adopted from18).
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The SWOT analysis presents the Strengths, Weaknesses, Opportunities, and Threats regarding the adoption of autonomous vehicles.

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Several countries, such as Denmark, the United States, and South Korea, have made notable advancements in creating legal frameworks for autonomous driving, but these frameworks often focus primarily on ethical codes and rules of conduct, leaving broader legal implications less addressed22. The European Parliament has acknowledged the challenges of integrating AVs into citizens’ daily lives, aiming to strike a balance between protecting citizens and fostering innovation. The EU seeks to develop a legislative framework that addresses these competing objectives, ensuring both safety and innovation23. A major initiative in this domain is the European Commission’s strategy, announced on May 17th, 2018, titled “On the Road to Automated Mobility: An EU Strategy for Mobility in the Future”. This initiative seeks to establish a coordinated platform for EU member states, facilitating the testing of autonomous vehicles on public roads and promoting collaboration between public and private sectors4. A report from the European Parliament24 raised the importance of an integrated EU legislative framework that encompasses all modes of transport, including maritime and air, while also emphasizing the need for personal data protection and clear rules regarding liability in the event of an accident. Ultimately, the EU aims to strengthen innovation and technological progress while ensuring safety, and social cohesion, and fostering an environment contributory to the development and testing of autonomous vehicles.

In the event of an accident involving automated driving, liability presents significant challenges, requiring the clarification of legal responsibilities. The traditional concept of conventional vehicle (CV) liability cannot be directly extended to fully autonomous vehicles. In conventional vehicles, liability typically falls on the driver, except when a vehicle defect is involved, in which case the manufacturer may be responsible, provided the driver was unaware of the defect25. A product is considered defective if it fails to meet expected safety standards26, and in such cases, the manufacturer assumes responsibility. However, with autonomous vehicles, where human involvement is minimal or absent, liability becomes more complex. It may fall on the vehicle manufacturer, the software engineer, or even the designer of the road network if infrastructure contributes to the accident27. Manufacturers are often shielded from liability for unknown defects, as evaluations are based on the scientific knowledge available at the time of production, not future developments26. The primary challenge lies in fitting automation into existing legal frameworks, further complicated by the classification of vehicle software as a product or a service. If classified as a product, liability may be assigned to the manufacturer for any malfunction, in accordance with EU Directive 85/374/EEC20. However, the evolving nature of software, in contrast to traditional physical goods, makes assigning liability more difficult28.

Privacy concerns related to AVs can be categorized into three subtypes: autonomy privacy, information privacy, and surveillance privacy29. Autonomy privacy refers to users’ ability to make independent decisions regarding the use of their data, with the risk that autonomy may be undermined as control shifts from the user to the vehicle’s technology. Information privacy concerns the protection of personal data, such as location and driver behavior, which could be misused if not properly protected. Surveillance privacy involves continuous monitoring of user activities by the vehicle, potentially violating privacy and freedom of movement29. While automation and connectivity are not fundamentally linked, they are expected to play a crucial role in the operation of autonomous vehicles30. Successful operation requires access to a wide range of data, such as weather conditions, roadworks, and traffic status5. This reliance on data raises privacy concerns, particularly as AVs operate across borders with varying data protection laws. The lack of a unified governance system for personal data protection intensifies these concerns31. In response, the EU has taken a leading role in global discussions on Artificial Intelligence (AI) ethics, with the General Data Protection Regulation (GDPR) addressing rights related to automated decision-making32. The European Commission’s advisory body on AI ethics has also proposed core principles for the development of AI technologies32. To balance the need for innovation with privacy protection, it is essential to address both legal and ethical concerns to foster trust in autonomous vehicles.

Additionally, public attitude plays a significant role in the adoption of AVs, as both support and opposition are influenced by societal, economic, and technological factors. According to a UK survey, 30% of the population supports autonomous vehicles, while 35% express opposition, with the remainder unsure33. Similarly, countries such as the Netherlands, Greece, the UK, and Germany have initiated pilot programs, but these have faced public resistance due to concerns regarding safety, privacy, and the potential for job losses. These issues underscore the importance of addressing public concerns and fostering trust in AV technology before widespread adoption can occur. Addressing citizens’ concerns regarding technological functionality, safety, and privacy will be key to ensuring the successful integration of autonomous vehicles into daily life across Europe.

Recent studies further verify the significant role of data protection, perceived safety, and infrastructure readiness in shaping public acceptance of AVs. Ho et al. (2023)34 provided a comprehensive review of perceived risks influencing AV acceptance, highlighting that privacy-related concerns, particularly data protection, and safety risks strongly affect user willingness to adopt AV technologies. Their review included European studies and underscored the multifaceted nature of perceived risk, encompassing both technical performance and broader societal implications. Complementing these findings, Kenesei et al. (2022)35 examined the impact of privacy concerns on AV acceptance within Hungary, surveying 949 participants. Their results emphasized that trust and perceived risk, especially regarding data protection, are critical determinants in shaping adoption intentions, pointing to the necessity of transparent and robust data governance frameworks to build public confidence.

In addition to privacy and safety perceptions, infrastructure readiness is a pivotal factor affecting AV adoption. Zefreh et al. (2023)36 investigated “facilitating conditions,” a key construct in the Unified Theory of Acceptance and Use of Technology (UTAUT) model, to understand infrastructure-related influences on AV usage intentions. Their large-scale study involving 1,823 participants across several European countries (Belgium, UK, Italy, Portugal, and Hungary) revealed that adequate infrastructure support and the perceived availability of necessary technological and environmental resources significantly affect acceptance levels. Furthermore, they identified that demographic and socioeconomic variables, such as age, gender, education, car ownership, and income, moderate these effects, highlighting the heterogeneous nature of AV adoption across different population segments and national contexts.

Together, these studies underscore the critical need for coordinated efforts to ensure data privacy protection, address safety concerns, and enhance infrastructure readiness to facilitate widespread AV adoption in Europe. Policymakers and stakeholders must consider these interrelated factors holistically to foster trust, reduce perceived risks, and create enabling environments that support the integration of autonomous vehicles into daily mobility.

The examined literature highlights the significant potential of AVs to enhance transportation systems by improving quality, safety, and environmental sustainability. However, the integration of AVs into European society presents complex challenges. Despite growing adoption in the EU, concerns over safety and trust—particularly among vulnerable groups like cyclists and pedestrians—remain critical barriers to acceptance37. Additionally, technological hurdles, legal uncertainties, and socio-economic factors further complicate widespread adoption. Understanding citizens’ attitudes is therefore essential for facilitating the successful deployment of AVs.

In recent years, a growing body of literature has explored the influence of socio-economic factors, such as age, gender, income, education, and occupation, on public acceptance of AVs38,39,40,41,42,43. These studies underscore the value of demographic segmentation in understanding AV adoption patterns. However, many focus on specific user groups or isolated variables, without offering an integrated view of how multiple socio-economic dimensions interact with issue-specific concerns to shape public attitudes toward AVs. At the same time, research on CCAM and Cooperative Intelligent Transport Systems (C-ITS) has highlighted the potential benefits of AVs, such as improved road safety, reduced driver stress, and environmental gains44. Nonetheless, several barriers to AV deployment remain, including necessary road infrastructure upgrades, regulatory uncertainties, data privacy concerns, and cybersecurity risks.

Empirical and decision-analytic studies have collectively shown that AV acceptance is influenced by a complex interplay of psychological, legal, technological, and social factors. Trust in automation and perceived safety remain among the most decisive factors45,46, particularly for groups more sensitive to risk, such as women, elderly individuals, and those with lower income levels39,47. Additionally, perceptions regarding data privacy, system transparency, and regulatory clarity play a significant role in shaping public sentiment38,48. Studies using multicriteria decision-making frameworks further highlight that barriers to AV adoption, such as lack of user acceptance, absence of industry standards, and regulatory uncertainty, are interrelated and should be addressed systemically48,49. Moreover, social dynamics such as peer effects, prior knowledge of AVs, and personal susceptibility to social influence add to the heterogeneity of adoption behavior50. Cost sensitivity and the expected willingness to pay for AVs also remain underexplored, despite their central role in shaping technology acceptance44.

These findings emphasize the necessity of a multi-dimensional and context-specific understanding of AV adoption that accounts not only for demographic indicators but also for issue-specific attitudes and social learning mechanisms. This study addresses that gap by providing a comprehensive analysis of AV acceptance across multiple socio-economic dimensions within the European context. An online questionnaire was developed to capture citizen attitudes across diverse age and income groups, education levels, and employment sectors. Drawing from insights in the literature, the survey investigates public responses to a range of AV-related themes, including safety, liability, technological trust, and data privacy. A key contribution of this study lies in quantifying which concerns most negatively affect AV acceptance, thereby establishing a hierarchy of barriers that can inform targeted policymaking and communication strategies. Unlike prior studies using multi-criteria or latent variable models, our analysis employs a binary logistic regression framework to evaluate how both demographic factors and thematic concerns influence acceptance. The resulting model identifies critical enablers and barriers to AV adoption and offers actionable insights to inform policy decisions and drive technological innovation, ultimately facilitating a smoother transition to autonomous mobility.

Methods

This section outlines this study’s design phases, including the survey design for data collection and processing, and the theoretical framework for the development and implementation of a mathematical model to analyze the adoption of autonomous vehicles. The model is based on binary logistic regression, supported by data collected from the online survey responses. This sample dataset enables the exploration of how economic, infrastructural, and legislative factors, along with individual characteristics, influence the likelihood of adopting autonomous vehicles, drawing on established transportation modeling approaches.

Our study was conducted in three phases (Fig. 2). First, an online survey with transport-related questions along with socio-demographic questions was developed in four languages (English, German, Spanish, and Greek) and distributed to participants in European countries. The collected information included socioeconomic data, mobility behavior data including travel modes and purposes, and data regarding the respondents’ preference for autonomous vehicles. Second, the data processing phase included data filtering and cleaning, and the transformation of the response data into a dataset suitable for modeling purposes. Lastly, the mathematical modeling phase included a detailed analysis of the transformed data and the implementation of the binary logistic regression model.

Fig. 2: Study design phases.
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The study design consists of three phases: an Online Survey, the Data Collection and Processing, and the Mathematical Modelling approach.

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The survey design included three main processes: Recruitment process, Survey questions, and Data collection process. To investigate the extent to which public opinion on automation is influenced by specific factors, an online survey was developed and administered. The recruitment process began in September 2023, and the survey remained available for one year, gathering a total of 235 responses by September 2024. The questionnaire was distributed digitally across Europe to improve reach and convenience, targeting individuals from diverse age groups and social backgrounds. While digital distribution facilitated broad geographic coverage, we acknowledge that it may have excluded individuals with limited internet access or digital literacy; groups that may also exhibit greater reluctance toward automation. Participation was voluntary, and confidentiality was maintained throughout the study.

The design of survey questions was guided by existing literature and prior empirical studies that identified key factors influencing public opinion on autonomous driving. Although no single existing questionnaire was replicated, the development process synthesized themes commonly addressed in earlier research. To accommodate a diverse audience, the survey began with a brief introduction defining autonomous vehicles. Respondents were asked about their perspectives on the adequacy of current transportation modes, their primary purposes for daily travel, and associated travel habits. Additional questions assessed participants’ knowledge of and familiarity with automation, as well as their general attitudes toward technological advancements. Specific topics included the likelihood of using an autonomous vehicle as a driver or passenger and preferences for operating such vehicles in different contexts, such as highways versus residential areas. The survey also examined five key factors influencing public attitudes toward autonomous vehicles: legislative framework, cost, road infrastructure, data privacy protection, and passenger safety. To ensure content validity, the draft questionnaire was reviewed by academic and industry experts in the field of transport behavior and autonomous vehicle technologies. Furthermore, a pilot survey was conducted with a small sample to test the clarity, structure, and comprehension of the questions. Based on the feedback received, minor revisions were made prior to full-scale deployment.

Data collection was conducted online throughout the duration of the study. Supplementary socio-demographic information, including gender, economic status, age, and professional situation, was collected at the end of the questionnaire. This data was essential for analyzing variations in attitudes across different population segments, ensuring the robustness of the survey findings. The online format facilitated efficient data recording and storage, enabling detailed analysis of public attitudes toward AVs. Additionally, the findings provided valuable insights into how governments and automobile manufacturers might effectively promote the adoption of this emerging technology.

In transportation modeling, individual decisions are aggregated to reflect collective behavior through measurable parameters and variables. This standardization provides a robust framework for analyzing average behaviors, despite the lack of a universally accepted choice theory. Alternative theories vary in how they idealize cognitive processes underlying observed behaviors3. These theoretical distinctions inform the analytical framework for studying the adoption of autonomous vehicles.

This research builds on prior studies that employ questionnaires and statistical modeling to investigate the adoption of autonomous vehicles. For example, Tan et al. (2020)51 analyzed key market determinants using logistic regression models on survey data. Similar studies, focused on Athens, Greece, examined factors affecting acceptance of automation and mobility services through logistic regression52, and utilized a multinomial logit model to analyze preferences for shared autonomous vehicle use, identifying critical influences on user choices53. These studies provide a foundation for understanding the role of statistical modeling in evaluating attitudes toward automation and mobility.

The methodological analysis of this study integrates theoretical foundations from the alternative choice theory, utility theory, and logistic regression modeling, providing a comprehensive approach to understanding transportation preferences and behaviors. The alternative choice theory framework by Ben-Akiva & Lerman (1985)3 conceptualizes decision-making as a sequence of problem definition, evaluation of alternatives, and choice implementation. This approach considers the decision-maker (individual or group), feasible and known alternatives, and decision rules such as dominance, satisfaction, and utility. Utility theory further refines the analysis by quantifying the attractiveness of choices. Utility maximization posits that individuals choose options offering the highest perceived benefit, expressed as a sum of deterministic and random elements3,54,55.

In line with these theoretical foundations, this study employs a binary logistic regression model to estimate the probability of adoption of autonomous vehicles based on various explanatory variables. This modeling choice is motivated by the binary nature of the dependent variable (adoption vs. non-adoption) and the moderate size of the dataset. Logistic regression provides a statistically comprehensive and interpretable framework suitable for small to medium sample sizes and is frequently used in transportation behavior studies.

The binary logistic regression model is employed to estimate the likelihood of respondents adopting autonomous vehicles based on explanatory variables such as socioeconomic and technological factors. The dependent variable \(Y\) represents the decision to adopt (1) or not adopt (0) autonomous vehicles, while the explanatory variables \(X\) capture influential factors. The logistic regression Eq. (1) is expressed as follows:

$$y={logit}\left(P\right)={\text{ln}}\left(\frac{P}{1-P}\right)={\beta }_{0}+{\beta }_{i}{x}_{i}$$
(1)

where \(P\) is the probability of adoption (\(y=1\)), \({\beta }_{0}\) is the model constant, \({\beta }_{i}\) are parameter estimates, and \({x}_{i}\) are explanatory variables. The model’s properties, including parameter estimation using sample data, are derived based on statistical criteria for reliability, consistency, and asymptotic behavior3.

Estimators are evaluated for their small sample and asymptotic properties, ensuring robustness and reliability3. This theoretical and methodological foundation enables the examination of key factors influencing the adoption of autonomous vehicles, offering valuable insights into individual preferences and market dynamics. In this context, binary logistic regression was selected due to its suitability for modeling binary outcomes and its robustness in the presence of a modest sample size. Nevertheless, we acknowledge that future research could benefit from more advanced modeling approaches, particularly when larger and more representative datasets become available. These may include multinomial or nested logit models to evaluate multiple choice alternatives, latent class models to capture unobserved heterogeneity, or machine learning techniques (e.g., random forests, support vector machines) to explore non-linear relationships and complex interactions. Additionally, structural equation modeling could offer insights into latent behavioral constructs. Incorporating such methodologies in future studies could further enhance the explanatory power and generalizability of findings related to public preferences on autonomous vehicle adoption. The subsequent sections build upon the above-described process providing a structured approach to collecting empirical data and analyzing user preferences concerning the adoption of autonomous vehicles.

Results

Building upon the findings of the above-reviewed literature and research on driving automation, an online survey was developed and conducted to gather questionnaire responses. The objective was to analyze and derive insights into factors influencing the adoption of fully automated vehicles. This includes the socio-demographic characteristics of the sample, as well as a detailed description of the data processing methods used to create the database, which served as the foundation for the analysis and the formulation of the conclusions discussed in this study.

First, we present the descriptive statistics of the sample collected. The questionnaire responses collected from various European countries are analyzed to then present the derived modeling results based on the online survey. The primary objective is to highlight the characteristics of the respondents, whose input forms the basis of the results. These individual characteristics encompass the physical, social, and behavioral attributes of the sample, providing critical insights into the adoption of AVs. The survey findings are based on a sample of \({\boldsymbol{n}}\)=235 responses from twelve countries across Europe.

The statistical analysis of the questionnaire data is summarized as follows, with results classified into four categories according to the questions posed to the participants:

  • Socio-Demographic Characteristics: Variations in the sample are analyzed based on gender, income, age, and employment status (Table 1).

  • Mobility Behavior: Insights into the adequacy, satisfaction, transport mode, and trip purpose of transportation among respondents (Table 2).

  • Perception of Autonomous Vehicles: Results cover the perception of using autonomous vehicles in urban and highway settings, attitudes toward autonomous public transportation, opinions on the cost of autonomous vehicles, and concerns about personal data management (Table 3).

  • Knowledge about Autonomous Vehicles: Statistics reflect respondents’ familiarity with autonomous vehicle technology, its usage, and its potential influence on their decisions (Table 4).

Table 1 Sociodemographic information.
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Table 2 Mobility behavior information of the respondents.
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Table 3 Respondents’ Perception of Autonomous Vehicles.
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Table 4 Respondents’ knowledge about Autonomous Vehicles.
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For most questions, respondents evaluated scenarios using a Likert scale ranging from 1 for the most negative answers (“totally dissatisfied”, “completely inadequate”, “absolutely not”, “complete ignorance”, “totally disagree”) to 5 for the most positive answers (“totally satisfied”, “completely adequate”, “absolutely yes”, “excellent knowledge”, “totally agree”).

First, the socio-demographic characteristics of the sample are summarized in Table 1. The gender distribution was balanced, with no significant differences between male and female respondents. The age distribution of the sample reflected a diverse representation of the typical workforce, with individuals over 65 years old comprising just 1% of the total respondents. Employment status revealed that over 65% of respondents were engaged in full-time work, while 30% identified as students, and approximately 2% were retired. Regarding economic status, 64% of respondents reported a monthly income below 1500€, whereas only 9% earned more than 3500€ per month.

Regarding the mobility behavior of the respondents, Table 2 reveals that approximately 60% commute for work or educational purposes. Additionally, 71% rely on either private vehicles or public transportation for their daily mobility needs, indicating a direct influence of automation on their choices. Dissatisfaction with available transportation options is observed among 22% of respondents, who rated their satisfaction as low (responses 1 to 2), while 40% expressed a neutral perspective (response 3). Concerning the adequacy of public transport in their local neighborhoods, dissatisfaction levels increase to 46% (responses 1 to 2).

Regarding respondents’ perception of AVs, Table 3 reveals that the majority of respondents hold a positive perspective on the use of driverless public transport, with 88% responding “Yes” or “Maybe,” while only 12% expressed complete opposition. Participants indicated a higher level of trust in highway AV use compared to city center use. Specifically, 29% showed a somewhat positive to positive attitude (responses 4 to 5) toward using autonomous vehicles in city centers, while 41% expressed similar views for highway use.

In terms of affordability, 71% of respondents confirmed that the cost of autonomous vehicles is a critical consideration (responses 4 to 5), and they should be accessible to all citizens. Finally, 38% of respondents indicated a high level of confidence (responses 4 to 5) that the protection of personal data significantly influences their decision-making process.

Table 4 shows a general lack of familiarity with respondents’ knowledge of autonomous vehicle technology. Specifically, 44% reported having little to no knowledge of AV technology, while 23% indicated full or near-complete understanding. This limited awareness appears to influence decision-making, as 29% of respondents (responses 4 to 5) stated that their lack of knowledge prevents them from considering the use of an autonomous vehicle.

When it comes to exposure to automated vehicles, the sample was divided: 55% reported never having driven such a vehicle, while 45% had prior experience. Regarding perceptions of safety, 23% of respondents expressed confidence or near confidence that conventional vehicles are safer than autonomous ones, whereas 34% believed the opposite, favoring autonomous vehicles in terms of safety.

In conclusion, the statistical analysis of questionnaire responses revealed several indicative trends relating to the broader relationship between socio-demographic characteristics and perceptions of autonomous vehicle technology. For example, respondents who reported higher levels of familiarity with AV technology were more likely to express a willingness, or at least openness, to use driverless transportation options.

These findings, while suggestive of meaningful patterns, are derived from a modest sample of 235 individuals from diverse European countries. However, the sample reflects uneven representation across some demographic categories, including age and income levels, which limits the generalizability of specific subgroup insights. Therefore, the observed trends should be interpreted as sample exploratory, not definitive. Further research with larger and more balanced samples is needed to validate and expand upon these early observations.

Regarding the Preference for Autonomous Vehicles, we examine the willingness of respondents to use autonomous vehicles, based on their evaluation of various factors. The questionnaire asked respondents to compare a series of scenarios in which these factors occurred simultaneously and to indicate whether they would choose to use an autonomous vehicle under those specific conditions. Five key factors were identified as most significant based on the literature review: protection of personal data, road infrastructure, legislation, vehicle cost, and passenger safety. For each of these factors, respondents were presented with scenarios in which the examined factor had a negative outcome while the others remained positive. The statistical results of the sample’s decisions, considering all possible combinations of these factors, are illustrated in the following figures.

As shown in Fig. 3, the legislative framework consistently had a greater influence on decision-making than the other factors, with the difference between yes and no answers averaging around 20% in three instances. The only exception to this pattern occurred in the case of safety, where the legislative framework’s impact was still significant but with a smaller difference of 6%.

Fig. 3: Scenario assuming an insufficient legislative framework.
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In this scenario, it is assumed that a sufficient legislative framework is lacking. Respondents were asked to state their preferences under the condition that the remaining four factors were considered favourable.

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Figure 4 demonstrates that road infrastructure is a critical factor influencing the choice to use an autonomous vehicle, with a notably large difference. In most scenarios, this difference in choices exceeds 40%, except when evaluating safety, where it reduces to just over 20%.

Fig. 4: Scenario assuming insufficient road infrastructure.
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In this scenario, it is assumed that road infrastructure is inadequate. Respondents were asked to express their preferences assuming the other four factors were favourable.

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Figure 5 reveals notable variability in the importance of the cost of AVs. Respondents prioritize safety and road infrastructure over cost, regard the legal framework as equally important, and consider privacy to be of lesser significance. Interestingly, while other factors are ranked higher in these scenarios, prior responses from Table 3 showed that 71% of respondents were confident or nearly confident (responses 4 to 5) that affordability is essential.

Fig. 5: Scenario assuming autonomous vehicles are financially unaffordable.
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In this scenario, it is assumed that autonomous vehicles are not financially accessible. Respondents were asked to state their preferences under the assumption that the other four factors were favourable.

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Figure 6 highlights passenger safety as the most critical factor by the respondents, with overwhelming support across all scenarios presented.

Fig. 6: Scenario assuming passenger safety is not guaranteed. In this scenario, it is assumed that passenger safety is not ensured.
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Respondents were asked to indicate their preferences under the condition that the other four factors were considered favourable.

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Additionally, Fig. 7 demonstrates that data privacy protection emerges as the preferred factor in most scenarios, though the differences are relatively minor, ranging from 5% to 10%. This suggests a divided attitude among the respondents on the importance of data privacy.

Fig. 7: Scenario assuming data privacy protection is not ensured.
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In this scenario, it is assumed that data privacy is not adequately protected. Respondents were asked to express their preferences assuming the other four factors were considered favourable.

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In summary, the statistical analysis of scenarios involving the five examined factors underscores passenger safety as the primary criterion in decision-making. Across all cases, the preference for safety exceeded 50%, with the difference reaching 66% when compared to data privacy protection.

Road infrastructure emerged as another critical factor, with an observed difference of 40% between “yes” and “no” responses. Similarly, the legislative framework consistently ranked higher than other factors in all scenarios, although the variations were less significant than those observed for passenger safety and road infrastructure.

In contrast, the sample showed a divided opinion on vehicle cost and data privacy protection. Regarding AV cost, respondents were not willing to purchase an autonomous vehicle even if their data privacy was assured. However, they were more inclined to consider purchasing one despite high costs if passenger safety was guaranteed (22% difference) or if there was adequate road infrastructure (8% difference). These findings align with the prioritization of safety and infrastructure as key factors.

Lastly, opinions regarding data privacy protection were divided across all scenarios. In cases where the difference between positive and negative responses exceeded 10%, the legislative framework played a decisive role, with the majority prioritizing legislation over data privacy (12% difference). Similarly, road infrastructure was considered more important than data privacy, with a difference of 14%. These results highlight the nuanced preferences of respondents in evaluating autonomous vehicle adoption.

Building upon the statistical analysis of the responses, the next step involves Modelling user Preferences for Autonomous Vehicles. Using the data collected from \(n=235\) online questionnaires, the primary objective is to analyze and evaluate the factors influencing European citizens’ decisions to adopt or use autonomous vehicles. Below, we present the applied modeling approach and results.

Once participants completed the online questionnaire, the necessary data was gathered for modeling purposes. Using questionnaire responses, variables related to respondents’ personal information, travel behavior, and knowledge of autonomous vehicles were examined to develop a binary logit model. Each response in the survey represented an individual’s evaluation of various choice scenarios, with each alternative clearly defined.

The dependent variable (\(Y\)) was defined as the respondent’s preference for using an autonomous vehicle or not for their daily commute. Specifically, Y=1 indicated a willingness/preference to adopt autonomous vehicles, while Y=0 signified a refusal. To assess the impact of the factors under consideration, the utility function was calculated. The data collected through the online survey was transformed into a long-format dataset, enabling the calculation of the utility function using the R programming language.

A correlation analysis was subsequently performed to examine the relationships among the variables under consideration. This method evaluates the strength and direction of associations between variables, with correlation coefficients ranging from -1 to +1. Values near zero indicate no or very weak correlation. In this study, all variables demonstrated either zero or very weak correlations with each other (Fig. 8). A few expected relationships showed relatively higher correlations, which serve to validate the internal consistency of the responses. These include:

  • The age group of 18–25 (age18_25) and student as professional status (ps_unistudent), with a correlation of 0.77.

  • The age group of greater than 65 (age_gr65) and retired as professional status (ps_retired), with a correlation of 0.70.

  • Travel purpose for education (Education) and student as professional status (ps_unistudent), with a correlation of 0.70.

Fig. 8: Correlation matrix of variables under consideration.
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The correlation matrix is used to illustrate the relationships between all variables of the dataset considered in the modelling approach. It shows the strength and direction of the linear relationship between each pair of variables.

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After evaluating multiple scenarios and combinations of factors associated with the examined variables and coefficient types, the utility function, as presented in Eq. (2), was determined to represent the model formula:

$$\begin{array}{l}Logit\left(p\right)={\text{ln}}\left(\frac{p}{1-p}\right)\\ ={\beta }_{0}+{\beta }_{AVknowledge3}* A{V}_{knowledge{3}}+{\beta }_{AVknowledge}* A{V}_{knowledge5}\\ +{\beta }_{drive{n}_{AV}}* drive{n}_{AV}+{\beta }_{AVsaferthanC{V}_{3}}* A{V}_{saferthanC{V}_{3}}+\,{\beta }_{AVsaferthanC{V}_{4}}\\ * A{V}_{saferthanC{V}_{4}}+{\beta }_{AVsaferthanC{V}_{5}}* A{V}_{saferthanC{V}_{5}}+{\beta }_{AVtrusthighwa{y}_{5}}\\ * A{V}_{trusthighwa{y}_{5}}+{\beta }_{dataprivac{y}_{AVpreference4}}* dataprivac{y}_{AVpreference4}\\ +{\beta }_{dataprivac{y}_{AVpreference5}}* dataprivac{y}_{AVpreference5}+{\beta }_{autonomou{s}_{P{T}_{Europe}}}\\ * autonomou{s}_{P{T}_{Europe}}+{\beta }_{Gender}* Gender+{\beta }_{age{36}_{45}}* age{36}_{45}+{\beta }_{incom{e}_{gr5000}}\\ * incom{e}_{gr5000}+{\beta }_{ag{e}_{gr65}}* ag{e}_{gr65}+{\beta }_{Mc}* Mc+{\beta }_{Entertainment}\\ * Entertainment+{\beta }_{Vehdr}* Vehdr+{\beta }_{Safety}* Safety+{\beta }_{Infrastructure}\\ * Infrastructure+{\beta }_{Framework}* Framework\end{array}$$
(2)

Where: \(p\): is the probability of using an Autonomous Vehicle,

\({\beta }_{0}\): alternative specific constant (or intercept) of the utility function,

\({\beta }_{i}:\) coefficient of variable i (beta parameters),

\({{\rm{AV}}}_{{\rm{knowledge}}3}\): 1 if the respondent has neutral stance on their knowledge (level 3) of AVs (dummy variable),

\({{\rm{AV}}}_{{\rm{knowledge}}5}\): 1 if the respondent has excellent knowledge (level 5) of AVs (dummy variable),

\({{\rm{driven}}}_{{AV}}\): 1 if the respondent or their relative has ever driven a car with automation elements (binary),

\(A{V}_{{safe}{{rthanCV}}_{3}}\): 1 if the respondent has a neutral stance (level 3) that driving an AV is safer than a Conventional Vehicle (dummy variable),

\(A{V}_{{safe}{{rthanCV}}_{4}}\): 1 if the respondent positively believes (level 4) that driving an AV is safer than a Conventional Vehicle (dummy variable),

\(A{V}_{{safe}{{rthanCV}}_{5}}\): 1 if the respondent totally agrees (level 5) that driving an AV is safer than a Conventional Vehicle (dummy variable),

\(A{V}_{{trus}{{thighway}}_{5}}\): 1 if the respondent totally trusts (level 5) an AV to drive on a highway (dummy variable),

\({dataprivac}{y}_{{AVpreference}4}\): 1 if the respondent positively believes (level 4) that data privacy issues affect the preference for an AV (dummy variable),

\({dataprivac}{y}_{{AVpreference}5}\): 1 if the respondent totally agrees (level 5) that data privacy issues affect the preference for an AV (dummy variable),

\({autonomou}{s}_{P{T}_{{Europe}}}\): 1 if yes for knowing that Autonomous PT modes are already/ currently being used in Europe (binary),

\({Gender}\): 1 for female, -1 for male, and 0 for diverse,

\({age}3{6}_{45}\): 1 if the respondent’s age is between 36-45 years old (dummy variable),

\({incom}{e}_{{gr}5000}\): 1 if the respondent’s monthly income is greater than 5000€ (dummy variable),

\({ag}{e}_{{gr}65}\): 1 if the respondent’s age is greater than 65 years old (dummy variable),

\({Mc}\): 1 if the respondent uses a Motorcycle as their main transport mode in their everyday life (binary),

\({Entertainment}\): 1 if the respondent’s main trip purpose of transportation in their everyday life is for entertainment (binary),

\({Vehdr}\): 1 if the respondent is driving a vehicle as their main transport mode in their everyday life (binary),

\({Safety}\): 1 if the respondent would prefer an AV when the passenger safety is guaranteed by the car industry (binary),

\({Infrastructure}\): 1 if the respondent would prefer an AV considering sufficient road infrastructure (binary), and

\({Framework}\): 1 if the respondent would prefer an AV considering an adequate legislative framework (binary).

In Table 5, the coefficients of the parameters included in the final utility function of the binary logistic regression model are presented. The analysis of the utility function results was conducted by examining the coefficients, their signs, and their significance levels, as follows:

  • Coefficient: In the discrete choice model (here, a binary logistic regression model), the coefficient represents its impact on the overall utility. A negative coefficient indicates that an increase in the corresponding factor reduces utility, while a positive coefficient suggests that an increase in the factor increases utility. Comparisons between coefficients are possible when binary coding is applied.

  • Significance Level (P[ | Z | >z]): This statistical parameter assesses the results of the Wald test, determining whether a parameter’s effect equals to zero. It evaluates the relevance of each variable in the utility equation of the discrete choice model. The significance level ranges from 0 to 1, where lower values indicate greater certainty about the variable’s impact. For instance, a value of 0.001 indicates that the coefficient is statistically significant with 99% confidence and a 0.1% error margin.

Table 5 Binary logistic regression model - Utility function coefficients
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The results in Table 5 demonstrate that all the characteristics included in the model for autonomous vehicle adoption are statistically significant at a 99% confidence level. Furthermore, the analysis of each parameter’s sign indicates that the observed behavior aligns with expectations, providing meaningful insights into the factors influencing decision-making.

More specifically, passenger safety emerges as the most decisive factor shaping European citizens’ attitudes toward road automation. The model reveals that ensuring passenger safety has a substantial positive effect on utility, confirming its critical role. This aligns with earlier statistical analyses and underscores safety as a non-negotiable priority for respondents. Regarding data privacy, the findings confirm that the absence of robust state and industry guarantees for data protection discourages the use of autonomous vehicles. Similarly, the legislative framework significantly influences decision-making; respondents favor adopting new technology only when the authorities ensure there are no legislative gaps or uncertainties. For road infrastructure, respondents are positively influenced by the existence of necessary infrastructure improvements that enhance passenger safety. Economic considerations also play a role. Respondents with a monthly income exceeding 5,000€ are more inclined to prefer an autonomous vehicle for their transport compared to those with lower income levels.

In addition to the above five key variables, several other statistically significant coefficients offer useful insights. For instance, individuals who are unaware of the presence of autonomous public transport systems in Europe show a lower likelihood of adopting AVs, underscoring the role of information dissemination. Female respondents exhibit a slightly higher inclination to adopt AVs, potentially reflecting greater sensitivity to safety and mobility benefits. Furthermore, individuals who travel primarily for entertainment purposes show increased interest in AV adoption, suggesting that trip purpose influences openness to new mobility technologies.

Additional model outcomes: Number of observations: 4700

Number of individuals: 235

Null loglikelihood (with zero coefficients): -3129.75

Loglikelihood at convergence: -2966.50

Number of Fisher Scoring iterations (to converge): 4

Moreover, a positive attitude towards new technology and prior familiarity with autonomous vehicles are significant factors. Individuals who either possess knowledge of AVs, have experience driving highly automated vehicles, or express greater trust in them compared to conventional vehicles, exhibit a strong positive influence on adoption. Conversely, a lack of awareness about existing autonomous public transport in Europe has a negative impact on decisions related to autonomous vehicle use.

Finally, socio-demographic characteristics further refined the analysis. Respondents aged 36–45 demonstrate a favorable inclination toward using autonomous vehicles, comparing with those over 65 years old, who are less supportive. Commuting habits also provide valuable insights. Respondents who primarily commute via motorcycles or cars as drivers exhibit a positive attitude toward the new technology. Similarly, those whose primary reason for commuting is entertainment express notable support for autonomous vehicles. These findings collectively highlight the multifaceted considerations influencing public adoption of autonomous vehicles, emphasizing the importance of safety, infrastructure, legal certainty, economic factors, and socio-demographic attributes.

These findings reinforce and extend existing research on the factors shaping AV acceptance. For example, the significance of safety, trust in automation, and regulatory clarity is consistent with previous work by Chen et al. (2022)46, Kaplan et al. (2019)45, and Al Mansoori et al. (2024)38, who identified these elements as essential for fostering public confidence in AVs. Our results further validate the significant influence of demographic factors such as income and age, aligning with the observations of Wang & Zhao (2019)39 and Alawadhi et al. (2020)47. Additionally, they confirm that legal uncertainty and concerns over data privacy continue to pose major obstacles, as highlighted by Raj et al. (2020)48. Importantly, this study contributes new perspectives by highlighting the role of information awareness and trip purpose, as significant determinants of AV adoption. For example, individuals traveling for leisure or those unfamiliar with existing AV services exhibit distinct attitudes, suggesting that exposure and communication strategies may strongly shape public readiness. Collectively, these findings highlight the importance of a more nuanced, context-specific understanding of AV acceptance in Europe and emphasize the need for comprehensive policy approaches that tackle both technological challenges and societal perceptions.

Discussion and conclusions

This study aimed to investigate the factors influencing European citizens’ decisions to adopt autonomous driving technology. As autonomous vehicles continue to attract attention from both automakers and governments, a key challenge lies in their smooth integration into citizens’ daily lives. This research highlights the significant impact that fully autonomous vehicles will have on the transport sector and explores the critical elements shaping public attitudes toward their adoption.

The study found that passenger safety is the most decisive factor influencing citizens’ willingness to adopt autonomous vehicles. Other important factors include road infrastructure, legislative frameworks, and data privacy protection. The analysis also revealed that socio-economic characteristics, particularly income, influence attitudes toward automation, with higher-income individuals showing greater support for autonomous vehicles. Respondents who have knowledge about this technology or have a favorable attitude toward automation are more likely to embrace its adoption, while those who commute for leisure or use cars and motorcycles also tend to be more supportive.

Based on these findings, several recommendations were made to facilitate the integration of autonomous vehicles into the transport sector. Prioritizing passenger safety is essential, with automakers urged to develop advanced risk detection systems. Additionally, protecting personal data through stringent security measures is critical for public trust. The road infrastructure must also be improved to accommodate autonomous vehicles, while legislative frameworks need updating to address liability and other legal concerns. Economic measures, such as subsidies or incentives, may be necessary to make autonomous vehicles more accessible to lower-income individuals. Finally, public awareness campaigns about this new technology are needed to educate citizens, particularly older age groups, about the benefits of autonomous vehicles.

Lastly, future research could focus on larger sample sizes to improve result reliability, as well as examine the adoption of autonomous vehicles in regions outside Europe to gain a global perspective. Additional factors, such as specific vehicle automation features (e.g., Brake Assist and Advanced Driver Assistance Systems - ADAS) or the influence of extreme weather conditions on trust in autonomous vehicles could also be explored. Expanding the socio-demographic profile to include information on vehicle ownership in households and long-distance travel habits would further enrich the analysis.