Impact Assessment of Governance Models on the Integration of Connected and Autonomous Vehicles
- Open Access
- 2026
- OriginalPaper
- Buchkapitel
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Abstract
1 Introduction and Background
The current requirements for frequent and driverless travel, combined with the evolution of technology, have led to the development of vehicle automation on a European level. The European Union (EU) estimates that the replacement of Conventional Vehicles (CVs) by Autonomous Vehicles (AVs) will occur within the following decades. Conversely, issues concerning the legal framework and road infrastructure have yet to be resolved [1]. The objective of this study is to review the existing advantages and barriers of adopting this new technology and to determine how the EU intends to resolve them. Finally, using a stated choice survey, we analyze and conclude the key factors influencing the European citizen's opinions and explore strategies to enhance automation adoption, mitigating its barriers.
Autonomous driving offers numerous advantages; however, integrating autonomous vehicles brings challenges like ambiguity over infrastructure, legal framework, and ethics, including concerns about personal data leaks [2]. Global collision legislation gaps make determining liability for operators or manufacturers challenging [3]. Economic hurdles include high costs, job shifts, and the potential rise of ridesharing and pay-as-you-go transport models [4].
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In Europe, the ongoing debate revolves around creating new tech-specific laws or revising existing ones [5]. Key goals are consumer protection and promoting innovation [6]. Integrating tech into legal frameworks necessitates international changes [5]. Despite rapid tech advancement, the EU aims to establish common rules, posing legislative challenges. Countries like Denmark, the US, and South Korea have already created legal frameworks for automated driving, focusing on ethics and guidelines [7].
The European Commission (EC) plans an EU-wide platform for testing autonomous vehicles in various transport modes, emphasizing data protection and accident responsibility [8, 9]. The EU AI ethics’ guideline is under development, emphasizing respecting human dignity and freedom of choice [10]. Balancing data protection with operational needs poses challenges in autonomy, information, and surveillance privacy [2]. In conventional vehicles, drivers are primarily liable for accidents, except for cases involving defects where manufacturers can be held accountable if drivers are unaware of the defect [11, 12]. Highly automated vehicles shift liability to software, involving manufacturers, software engineers, or road designers if they significantly influence vehicle movement [11]. There is ambiguity about whether automation fits within existing legal frameworks, and whether vehicle software is considered a service or product.
To enhance road safety, Europe needs harmonized traffic rules and innovative infrastructure, given unmanned vehicles sharing roads with others [10]. The EC introduced in 2016 a Strategy for Cooperative Intelligent Transport Systems (C-ITS) to align EU investments and regulations, enabling effective information sharing among road users and traffic management [1].
Societal Acceptance of CAVs
The societal acceptance of CAVs depends on multiple factors like public awareness, perception, safety concerns, and cultural differences. Despite increased awareness, the concept of CAVs remains poorly defined, leading to confusion about their benefits and risks. Safety is a major concern, with many skeptical about machines’ ability to navigate traffic safely. Acceptance varies by demographics, with younger individuals generally more open to CAVs compared to older generations [13‐15].
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Machine learning techniques have been used to forecast AV adoption rates by examining users’ perceptions and their intention to adopt CAVs. Factors influencing adoption include safety, trust, privacy, accessibility, and ethics, with surveys confirming these factors’ strong impact. Notably, Fuzzy Logic outperformed other classifiers in predicting user adoption, highlighting its potential in understanding public attitudes [16]. This aligns with findings that trust, compatibility, and perceived usefulness significantly influence acceptance, with social influence and personal innovativeness playing crucial roles [14].
Several studies emphasize shifting AV research from technical aspects to broader societal impacts, including societal acceptance, societal implications, and governance of the AV transition. This holistic approach shifts focus from consumer adoption to citizen acceptance, considering long-term impacts like accessibility, well-being, social inclusion, and public health. Participative governance is advocated to balance AV deployment with desirable future outcomes for citizens [17, 18].
Public engagement is crucial in building trust in AV technology. A lack of knowledge among non-experts contributes to trust deficits. Enhancing public understanding of CAVs’ benefits and involving the public in development from the design phase can foster trust and acceptance. Practical demonstrations and engaging diverse user groups through workshops, training, public consultations, and trials are effective measures [15]. Practical experience with AVs can help the public appreciate their benefits and address safety, legal responsibility, and privacy concerns [14, 15].
Research initiatives in Europe and the US focus on AVs’ transformative impacts on society, the economy, and the automotive and public transport industries. Efforts aim to improve environmental sustainability, collision avoidance, and traffic management while addressing safety, security, privacy, and ethical issues. Current efforts include integrating security measures into safety-critical systems and developing automotive cybersecurity standards to ensure safe and secure AV systems [19]. These initiatives address the complex challenges posed by AVs and ensure their safe and beneficial integration into society [19].
In conclusion, the studies collectively highlight that societal acceptance of AVs depends on multiple factors such as safety, trust, privacy, accessibility, and ethics. Effective regulation, public involvement, and clear communication are essential for fostering acceptance. Common themes include the need for trust-building measures, practical demonstrations, and addressing safety and ethical concerns. However, limitations include the emerging stage of AV technology, lack of empirical data, and the need for long-term impact assessments.
2 Data Collection, Results and Analysis
To assess European citizens’ views on adopting highly automated vehicles, a Stated Preference (SP) survey was conducted with 171 participants [20]. The survey, developed with European partners, was translated into English, Greek, Spanish, and German.
In addition to the SP survey, the questionnaire collected sociodemographic data, mobility patterns, and perceptions of AVs. Topics included satisfaction with current transport options, public transport adequacy, primary transport modes, and trip purposes. Questions about autonomous vehicles covered safety, trust, economic considerations, and data privacy. The SP survey assessed five key parameters: affordability, passenger safety, data privacy protection, road infrastructure, and legislative frameworks for CAVs. Respondents chose their preference for AVs when one parameter was negative and the other four were positive, answering four “Yes” or “No” questions without constraints.
The socio-demographic profile and key findings on respondent mobility are presented in Table 1 to understand factors impacting AV acceptance. The online survey included diverse respondent categories: 60% employees, 25% university students, and 10% self-employed. Gender split: 53% female, 45% male, 2% diverse. Age distribution: 85% university students or employees, few under 25 or over 56. Respondents represented ten countries: Belgium, Germany, Greece, Italy, Luxembourg, Netherlands, Portugal, Slovenia, Spain, and the UK.
Table 1.
Sociodemographic information
Sociodemographic | Responses, N = 171 | |||||
|---|---|---|---|---|---|---|
Gender | Male 45% | Female 53% | Diverse 2% | |||
Age | 18–25 | 26–35 | 36–45 | 46–55 | 56–65 | >65 |
22% | 40% | 14% | 16% | 7% | 1% | |
Professional status | University student | Employee | Self-employed | Unemployed | Retired | |
25% | 60% | 10% | 4% | 2% | ||
Monthly Income | <1000€ | 1000–1500€ | 1500–2500€ | 2500–3500€ | 3500–5000€ | >5000€ |
36% | 26% | 18% | 11% | 6% | 2% | |
Regarding respondent mobility (Table 2), satisfaction with available transport modes is generally high, though public transport adequacy shows dissatisfaction, with 14% finding it completely inadequate and 30% rather inadequate. Travel purposes include 46% primarily for work, 17% for entertainment, 12% for education, and 12% for family duties. Main transport modes are 35% public transport, 31% driving, 6% as passengers, 20% walking, and 5% cycling.
In terms of autonomous driving knowledge and perception, 47% claimed ignorance, while only 6% had complete knowledge, and 58% had not driven an automated vehicle. Regarding safety, respondents tended to agree that AVs are safer than conventional ones. Additionally, 46% were willing to use driverless public transport, with 41% expressing potential interest.
Table 2.
Mobility behavior information of the respondents
Mobility behavior | Responses, N = 171 | |||||
|---|---|---|---|---|---|---|
1 = totally dissatisfied | 2 | 3 | 4 | 5 = totally satisfied | ||
Satisfaction with transport modes | 4% | 19% | 40% | 29% | 8% | |
Adequacy of PT service | 14% | 30% | 27% | 21% | 7% | |
Main transport mode | Vehicle as driver | Vehicle as passenger | Public urban transport | Motorcycle | Bicycle | On foot |
31% | 6% | 35% | 4% | 5% | 20% | |
Main trip purpose | Work | Education | Entertainment | Leisure trip | Shopping | Family duties |
46% | 12% | 17% | 4% | 7% | 12% | |
Moreover, respondents were asked about trust in autonomous vehicle operation in city centers and on highways (Table 3). Highways garnered more trust compared to city centers. Economic affordability is key, with 51% saying it must be accessible to all, and 22% saying it should be. Two other factors considered for AV preference were data privacy and user familiarity. Additionally, 76% knew of autonomous public transport in European countries.
Table 3.
Preference for Autonomous Vehicles
Autonomous vehicle | Responses, N = 171 | ||||
|---|---|---|---|---|---|
Preference | 1 = absolutely not | 2 | 3 | 4 | 5 = absolutely yes |
Driving in a city center | 9% | 30% | 27% | 27% | 7% |
Driving on a highway | 11% | 19% | 28% | 35% | 7% |
Economically accessible to all | 5% | 5% | 16% | 22% | 51% |
Data privacy issues | 18% | 13% | 30% | 27% | 12% |
Ignorance of AVs | 15% | 15% | 30% | 22% | 9% |
As mentioned earlier, the stated preference experiment assessed five factors influencing autonomous vehicle adoption: affordability, safety, data privacy, road infrastructure, and legislative framework for CAVs. For each, a scenario with one unfavorable aspect and four positives was presented. Results are summarized in Table 4.
Table 4.
Preference on choosing an Autonomous Vehicle considering different factors.
Table showing preferences for autonomous vehicles under various conditions. Rows list scenarios where certain conditions are not met, such as financial affordability, legislative framework, road infrastructure, safety guarantees, and data privacy. Columns indicate conditions like financial affordability, legislative framework, road infrastructure, safety guarantees, and data privacy. Percentages represent the proportion of respondents who would still prefer autonomous vehicles under these conditions. Higher "Yes" percentages are highlighted in green.
The table above reveals safety as the primary factor influencing European citizens’ preference for autonomous vehicles. Over 80% would not choose one unless it is guaranteed safe, and 85% would not even if it is economically affordable. Road infrastructure is the second most important factor, with preferences ranging from 61% (with passenger safety guaranteed) to 76% (with affordability). Adequate legislation is crucial, with 64% avoiding AVs, even with reasonable prices, and 60% being cautious despite data privacy guarantees. Data privacy has mixed effects; 55% would abstain even with good road infrastructure, and 51% would do so even if passenger safety is assured. On the other hand, data privacy protection varies: 57% prefer AVs for safety even at the expense of data privacy. However, 53% resist even with sufficient legislation. Economic accessibility matters least; 60% would choose AVs if safe, 55% with good infrastructure, and 51% with strong legislation. Nonetheless, 53% would not choose AVs even with data protection.
3 Conclusions and Future Research
This paper aims to uncover the benefits and challenges of implementing CAVs and explore the EU's mitigation strategies. We examined legislative frameworks, with a focus on European cases. We further reviewed studies on the societal acceptance of CAVs. Five key factors affecting CAV acceptance emerged: passenger safety, road infrastructure, data privacy, legislation, and affordability. To assess the governance models’ impact on CAV integration and understand adoption factors, we conducted SP surveys focusing on the five CAV utilization obstacles. We then analyzed survey results, emphasizing sample socio-demographics, daily mobility, and attitudes toward these influencing factors. Survey participants prioritize passenger safety as the most crucial aspect when considering autonomous vehicles. Governments should also focus on road infrastructure sufficiency and robust legislative frameworks for accidents or data privacy issues. Economic affordability, while important, is a consideration for most citizens, who believe CAVs should be accessible to everyone.
Based on our literature review and SP survey findings, on CAV regulatory frameworks and influencing factors, we suggest the following research directions:
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Develop a binary logit model to quantify each influencing factor's impact on CAV acceptance.
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Enhance sample socio-demographics with vehicle ownership and trip mode data.
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Conduct cross-country or regional comparisons of the results.
Acknowledgments
This research was funded by the European Union’s Horizon 2020 CONDUCTOR project. The CONDUCTOR project has received funding from the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101077049.
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