Drivers often misjudge the capabilities of Advanced Driver Assistance Systems (ADAS), compromising safety. Guided by a Context–Vehicle–Driver (C-V-D) framework drawn from 22 empirical studies, this study analyzed a secondary survey of 838 drivers to identify predictors of self-reported “ADAS unawareness” (“I don’t know if I use it”). Analysis of the representation ratio (RR) showed that drivers with a low annual driving distance (' 5000 km), lack of private car ownership, and young age (18–29 years) were consistently overrepresented among unaware users (RR ≥ 1.2), while car sharing frequency and license tenure were not. Un-awareness was highest for Adaptive Cruise Control (ACC) among the three ADAS examined. These results support a hierarchical account in which contextual factors outweigh vehicle and driver-level influences. The C-V-D model yields testable hypotheses for road type, traffic density, and interface design that merit evaluation in larger-sample studies. Addressing the priority groups identified here can help designers, dealers, and educators reduce mode confusion and promote safe ADAS adoption.

Many drivers misjudge what their vehicle’s automation systems can actually do. This mismatch, known as mode confusion, can turn small misunderstandings into fatal consequences. Research has long examined drivers’ mental models and drivers’ confidence in engaging Advanced Driver Assistance Systems (ADAS), treating both as key contributors to mode confusion. Yet one crucial question remains largely unaddressed: do drivers know, correctly and confidently, which automation features are installed in their own vehicles? To address this question, we surveyed 1,487 U.S. vehicle owners whose manufacturers list Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA) as standard equipment. Each respondent’s self-reported ownership awareness was compared with external model-trim data. Despite generally high ownership confidence, 17.1% incorrectly believed their vehicle lacked ACC and 29.4% believed it lacked LKA. Ownership awareness is uneven across ADAS: LKA is misjudged more often than ACC, even among drivers who are confident in their ownership judgments. Specifically, owning an older vehicle is associated with lower ownership accuracy and lower ownership confidence, while exposure to demanding trip contexts is more strongly related to lower ownership confidence than to lower ownership accuracy. Analyses of self-reported reasons using Holm-adjusted Fisher tests and association-rule mining reveal why ownership-awareness misalignment occurs. Misaligned ownership awareness commonly co-occurs with a lack of information and a lack of first-use experience, often coupled with an acceptance barrier that may reflect reluctance to engage initially with ADAS. In contrast, correct-and-confident ownership awareness co-occurs with prior ADAS use, clear in-vehicle feedback, and dealer explanation. Taken together, our findings suggest opportunities to help mitigate early mode confusion, including enhancing feedback and status visibility in in-vehicle interfaces and supporting guided first use through sales interactions or in-vehicle onboarding experiences, both of which warrant further testing.

This study applies an extended version of one of the most popular technology acceptance models, the Unified Theory of Acceptance and Use of Technology (UTAUT2), to predict user acceptance of SAE Level 3 conditional automated driving among more than 9,000 car drivers from nine European and non-European countries. We extend the model by two factors, trust and teaming, that we consider pivotal for user acceptance of conditional automated driving. We also investigate the factors impacting the determinants of acceptance and use of conditional automated driving, addressing a well-known gap in research. In this study we find that 40% of respondents did not intend to buy, and 39% of respondents did not express an intention to use conditional automated driving when available. 71% of respondents indicated a preference to stay engaged in the driving task to respond to requests from the car to resume manual control. The structural equation modeling analysis revealed that performance expectancy is the strongest predictor of driver’s behavioral intentions to use conditional automated driving, followed by trust and social influence. Contrary to common beliefs positioning trust as one of the most influential drivers of user acceptance of AVs, the influence of trust on behavioral intention to use conditional automated driving is small. The availability of facilitating conditions supporting the use conditional automated driving (e.g., knowledge, getting help from friends, family, or car dealers) has a small influence on the acceptance of AVs. We also found significant effects of the factors impacting the determinants of acceptance and use. The effect of performance expectancy on hedonic motivation is positive, suggesting that the perceived usefulness positively enhances the perceived enjoyment. Similarily, the effect of social influence on performance expectancy and trust is positive, suggesting the social network of the individual plays an important role in promoting positive beliefs about the effectiveness of the technology and trust in the technology. Access to participation in the questionnaire was limited to respondents with access to internet, which is why future research should be performed with respondents without internet accessibility to examine differences in attitudes and conditional automated driving acceptance between these internet-affine and less internet-affine groups.

Shared autonomous shuttles (SASs) could improve the mobility infrastructure in the worlds’ growing cities. This novel service could reduce congestion and improve both mobility and sustainability. To facilitate the implementation of SASs, more research is needed on the psychological aspects of sharing a small, intimate shuttle with strangers. The current study is among the first to use open-ended questions to investigate SAS acceptance. This investigation is based on the Multi-Level Model on Automated Vehicle Acceptance (MAVA). We had 236 participants answer short-form interviews including both open-ended questions and quantitative items. Quantitative data were analyzed using descriptive statistics and correlations, and qualitative data analyzed with directed content analysis. Respondents seem very positive about the proposed new transport service. We found that perceived usefulness, hedonic motivation, trust, and social influence shared large correlations with intentions to use. Other factors such as demographics, technology savviness and use of public transport did not share a linear relationship with intentions to use. Qualitative analysis suggests that, while most people do not mind sharing shuttles with strangers, some could find the social situation deterring. People seem most concerned with availability, effectiveness, travel cost and safety. The reported positive attitudes towards the service seem predicated upon trust in the government regulation and proper testing of the technology, that many think of as immature. Regulation and thorough testing may be paramount in keeping people positive. This study emphasizes the importance of trust and safety to adoption of SAS, while suggesting new factors that need further investigation.

Introduction: Shared automated vehicles (SAVs) could significantly enhance public transport by addressing urban mobility challenges. However, public acceptance of SAVs remains under-studied, particularly regarding how informational factors and individual personality traits influence acceptance. Methods: This study explores SAV acceptance using data from an experimental survey of 1902 respondents across Norway. Participants were randomly presented with different informational conditions about SAV services, manipulating vehicle autonomy (fully autonomous vs. steward onboard), seating orientation (facing direction of travel vs. facing other passengers), and ethnicity of co-passengers. Personality traits from the Five Factor Model (FFM) and Social Dominance Orientation (SDO) were assessed. The General Acceptance Factor (GAF), derived from the Multi-Level Model of Automated Vehicle Acceptance (MAVA), was used as the primary outcome measure. Results: No significant main or interaction effects were found from the experimentally altered information conditions. However, personality traits significantly influenced acceptance. Specifically, higher openness and agreeableness positively predicted SAV acceptance, while higher neuroticism and social dominance orientation negatively predicted acceptance. Discussion: The absence of experimental effects suggests either a limited role of the manipulated factors or insufficiently robust manipulations. Conversely, the substantial impact of personality traits highlights the importance of psychological factors, particularly trust, openness, and social attitudes, in shaping SAV acceptance. These findings emphasize the need for tailored communication strategies to enhance SAV uptake, addressing specific psychological profiles and fostering trust in automation.

The investigation of automated vehicle acceptance (AVA) has received considerable attention in the past few years. Understanding the factors impacting their acceptance is pivotal to ensure a large-scale and wide acceptance of AVs. The AVA by pedestrians is still little understood. To address this knowledge gap, the main objective of this study is to develop and validate an instrument for the assessment of AVA by pedestrians. We tested this instrument on a German sample of pedestrians (n = 136), considering their individual demographic characteristics, and level of affinity for technology interaction. A four-step approach was adopted to analyze the data. First, a principal component analysis was performed to reduce the number of items, exploring the sources of variation in the dataset. Second, the principal components were subjected to a confirmatory factor analysis to investigate the validity and reliability of the proposed measurement model. Third, structural equation modeling was conducted to estimate the path relationships between our constructs. The study has revealed differences between the effect sizes and significance levels of the factors influencing pedestrians’ AVA. The AVA by pedestrians was most strongly influenced by affinity for technology interaction (i.e., extent to which the individual actively approaches or avoids the interaction with new systems), performance expectancy (i.e., extent to which the individual believes that using the system will support them in achieving gains in the performance of the task) and social influence (i.e., extent to which the individual believes that people important to them think that the individual should perform the behavior). Male pedestrians were more likely to accept AVs. We also revealed significant interaction effects of age on the variables in our model. With this work, we have contributed to the development and validation of the Automated Vehicle Acceptance Questionnaire for Pedestrians (AVAQ-P). We recommend future research to replicate the study with a larger, more representative and gender-diverse population of pedestrians, considering cross-cultural differences in AVA.

Understanding older adults' overall expectations about automated vehicles (AVs) is crucial for inclusive designs. The work-in-progress presents an exploratory study based on semi-structured interviews with 27 older adults in the Netherlands. A thematic analysis revealed an open-minded attitude towards AVs, optimism for improved safety, and pragmatic concerns about reliability. Participants expected AVs to be "well-behaved", delivering safe, predictable, and socially considerate driving styles. Participants also showed a desire for AVs to be communicative, providing feedback to reduce uncertainties. The findings provide implications for inclusive AV designs.

Shared automated vehicles (SAVs) may transform urban mobility but face strong public resistance. Existing acceptance research is fragmented and often relies on complex frameworks. We introduce a simplified shared automated vehicle acceptance (SAVA) model, identifying trust, utility and social comfort as core predictors of SAV acceptance. Using structural equation modelling, we tested whether these factors form a general acceptance factor (GAF) or operate as distinct but correlated predictors of intention to use. A 2 × 2 × 2 experimental design further assessed whether targeted informational interventions could increase trust, utility and social comfort. Monte Carlo-based power analyses indicated a minimum of 800 participants to detect small effects (Cohen's f = 0.20) with 80% power at α = 0.05; we recruited 1250 respondents after data cleaning, ensuring adequate power. Results show that trust, utility and social comfort are best modelled as distinct but correlated constructs, implying rather than establishing a GAF. Utility exerted the strongest effect. Experimental manipulations had no significant impact, suggesting stronger interventions are needed to shift acceptance. The SAVA model provides a parsimonious, testable framework explaining intention to use SAVs. This recommended registered report advances theory and offers practical insights for policymakers and providers seeking to improve SAV acceptance.

Automated vehicles pose a unique challenge to the safety of vulnerable road users. Research on cyclist-automated vehicle interaction has received relatively little attention compared to pedestrian safety. This exploratory study aims to bridge this gap by identifying cyclist-automated vehicle scenarios and providing recommendations for future research. In this study, we triangulated three sources: a systematic literature review of previous research on cyclists and automated vehicles, group interviews with eight traffic safety and automation experts, and questionnaire data. The resulting scenario collection comprised 20 prototypical scenarios of cyclist-automated vehicle interaction, grouped into four categories based on the road users’ direction of movement: crossing, passing, overtaking, and merging scenarios. The survey results indicated that right-turning vehicles, dooring scenarios, and more complex situations have the highest likelihood of accidents. Passing and merging scenarios are particularly relevant for studying automated vehicle communication solutions since they involve negotiation. Future research should also consider phantom braking and driving styles of vehicles, as well as programming proactive safety behaviours and designing on-vehicle interfaces that accommodate cyclists.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 31 December 2023

DOI: 10.1016/j.trip.2023.100986

User comfort in higher-level Automated Vehicles (AVs, SAE Level 4+) is crucial for public acceptance. AV driving styles, characterised by vehicle kinematic and proxemic factors, affect user comfort, with “human-like” driving styles expected to provide natural feelings. We investigated a) how the kinematic and proxemic factors of an AV's driving style affect users' evaluation of comfort and naturalness, and b) how the similarities between automated and users' manual driving styles affect user evaluation. Using a motion-based driving simulator, participants experienced three Level 4 automated driving styles: two human-like (defensive, aggressive) and one machine-like. They also manually drove the same route. Participants rated their comfort and naturalness of each automated controller, across twenty-four varied UK road sections. We calculated maximum absolute values of the kinematic and proxemic factors affecting the AV's driving styles in longitudinal, lateral, and vertical directions, for each road section, to characterise the automated driving styles. The Euclidean distance between AV and manual driving styles, in terms of kinematic and proxemic factors, was calculated to characterise the human-like driving style of the AV. We used mixed-effects models to examine a) the effect of AV's kinematic and proxemic factors on the evaluation of comfort and naturalness, and b) how similarities between manual and automated driving styles affected the evaluation. Results showed significant effects of lateral and rotational kinematic factors on comfort and naturalness, with longitudinal kinematic factors having a less prominent effect. Similarities in vehicle metrics, such as speed, longitudinal jerk, lateral offset, and yaw, between manual and automated driving styles, enhanced user comfort and naturalness. This research facilitates an understanding of how control features of AVs affect user experience, contributing to the design of user-centred controllers and better acceptance of higher-level AVs.

Sharp curves in freeways are known to be unsafe design elements since drivers do not expect them. It is difficult for drivers to estimate the radius of a curve. Therefore, drivers are believed to use other cues to decelerate when approaching a curve. Based on previous successful experiences of driven speeds in curves, drivers are thought to have built expectations of safe speeds given certain cues, minimalising risks. This research employs a Bayesian Belief Network to model driver expectations using measured speeds in 153 curves and data on the characteristics of the curve approaches. This model mimics expectations as the probability of measured speeds given certain cues. Using Bayes theorem, prior beliefs on safe speeds are updated towards a posterior belief when a new cue is observed during curve approach. We refer to this posterior belief as expected safe speed. Drivers are assumed to adjust their operating speed if it does not match their expected safe speed. The model shows that the visible deflection angle has a large influence in setting the expectations of a safe speed for an upcoming curve. In addition, the preceding type of roadway and the number of lanes are both important cues to set a driver's expectations of a safe speed. Speed and warning signs are shown to be interdependent on the road scene and hence have less influence in setting expectations. This research shows that design and safety assessment of freeway curves should be considered aligned with the road scene upstream of the curve.

Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.

This study investigates acceptance of shared autonomous shuttles (SASs) in a suburban area. A model where contextual variables were mediated through trust in SASs and technology optimism was tested. We examined intentions to use SASs without a steward and the significance of social distancing. Data were collected at the start and end of a 2020–2021 pilot involving 922 and 608 participants respectively, operating at SAE level 3. Findings indicate that trust and technological optimism significantly influence the willingness to use SASs, though contextual variables show minimal impact. Older adults and women displayed lower trust and optimism, reducing their usage intentions. These two groups also feel that it is more important to be able to keep social distance while riding SASs. The study suggests that future pilots should avoid negative impacts from using immature technology and address the social needs of specific groups.

The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs. The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs. Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.

The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV's driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV's capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.