The rise of ChatGPT has prompted concerns over users’ agency when revealing personal data to artificial intelligence. This study examined users’ likelihood of disclosing their data to ChatGPT in physical and mental health scenarios. Participants (N = 216) completed a repeated measures survey where they viewed four vignettes of hypothetical scenarios and were asked to imagine disclosing health information (physical and mental health) at two sensitivity levels (low and high self-disclosure). A repeated measures ANOVA revealed participants were significantly more likely to provide their data when the information required low-disclosure than high-disclosure. Furthermore, participants were significantly more likely to report uploading their health information in the physical health scenario than in the mental health scenario. The findings suggest ChatGPT users exercise caution in disclosing data to the platform. Reluctance to upload information in sensitive scenarios reduces the training data for large language models, resulting in potential stagnation in technology development.

Mobile phone distraction is a critical global road safety issue, contributing to crashes and subsequent injuries and fatalities. This issue has led to calls for effective interventions. Based on neuropsychological research indicating that colour stimuli play a significant role in driving phone engagement, one potential strategy to reduce road user phone use while on the road is activating greyscale on phones. By removing colour, the sensory reward associated with phone use may be diminished, potentially reducing usage. However, this approach has yet to be empirically tested. As such, the aim of this study is to investigate how greyscale influences phone use behaviours while driving and walking. Participants were asked to switch their phone interface from colour to greyscale for a duration of 2-weeks. A mixed-methods approach, including surveys and interviews, was employed to gather insights from participants regarding their perceptions of greyscale on their phone use behaviour while driving and walking. The quantitative results showed that greyscale decreased the frequency of participants glancing at their phone screens in a cradle while driving. However, using the greyscale feature did not lead to significant changes in the frequency of participants picking up the phone and looking at the screen while driving, nor did it increase participants’ use of other devices such as the in-vehicle infotainment system, smartwatches, or voice commands. Additionally, greyscale significantly reduced the probability of pedestrians using handsfree phones while walking, although greyscale did not influence the likelihood of looking at the screen of a handheld phone. The qualitative results revealed that the greyscale had a complex impact on road users’ phone behaviour. Greyscale altered how they used their phones, made them less appealing and enjoyable, and added complexity to phone use. However, some participants found work-around, though not everyone adopted them. Overall, the findings suggest that while greyscale effectively reduced some phone-related behaviours over a 2-week period, its impact on phone use behaviours while driving or walking was limited in scope, with mixed effectiveness across different contexts and with some users finding work-around.

The rise of ChatGPT has emphasized the need for an improved conceptual understanding of users’ agency when interacting with artificial intelligence (AI) systems for healthcare. Australian ChatGPT users (N = 216) completed a repeated measures online survey. Hierarchical regression analyses assessed the influence of demographic factors (age and gender), Technology Acceptance Model constructs (perceived usefulness and perceived ease of use), and extended variables (trust, privacy concerns) on users' behavioral intentions to use ChatGPT for physical and mental healthcare. The proposed model was partially supported: the findings emphasized the need to establish user trust in ChatGPT and its perceived usefulness in both areas of healthcare. Privacy concerns were a significant predictor of intentions to use ChatGPT for mental healthcare with perceived ease of use predicting intentions to use ChatGPT for physical healthcare. The findings indicate predictors of uses of AI cannot be generalized across healthcare types and unique drivers should be considered.

Distracted driving continues to be a significant risk to road safety. While effective legislation and education are essential for preventing distracted driving, there is evidence showing that current official information and road rules related to distracted driving may not provide drivers with a comprehensive understanding of the issue. Therefore, this study aimed to explore drivers’ perspectives on how official information and road rules could be improved to effectively address distracted driving. Semi-structured interviews were conducted with 35 drivers from Queensland, Australia, with thematic analysis employed to extract key themes from the discussions. The results showed that official information on distracted driving could benefit from more comprehensive content, including underrepresented distraction sources (e.g., using a smartwatch, interacting with passengers, and looking at advertisement billboards), the safety risks, and impacts on driving performance. Participants emphasised the need for distracted driving legislation to address the risks posed by a broader range of technological devices (e.g., infotainment systems, wearable devices), not just hand-held mobile phones. The results suggested a need for improved presentation of information, with future distracted driving content suggested to be presented visually, along with more targeted messaging for high-risk drivers such as young drivers. Further, social media, short training, and outdoor media were perceived by participants as the most effective delivery mechanisms for distracted driving resources. The findings provide valuable guidance for policymakers in establishing and communicating information and road rules for distracted driving.

Empirical data demonstrates that distracted driving is a leading cause of crashes even in countries with sophisticated road safety systems. As such, a paradigm shift is needed to prevent driver distraction. This study aims to contribute to this paradigm shift by critically investigating the official distraction-related information and road rules for drivers in Australia, while gaining an understanding of how distraction is specifically addressed in these materials. Using a multistage content analysis, it was identified that official information focuses on three major categories including overview of distraction, sources of distraction, and prevention of distraction. The findings suggested ways that State Governments could improve the available information and road rules, as some of these materials were insufficient or ambiguous. For instance, several sources of distraction, particularly internal distractions (e.g., medical impairments) and external distractions (e.g., advertisement billboards) have been overlooked or received limited attention in the information. Additionally, the information does not address the specific needs of certain road users, such as young and inexperienced drivers. Further, the guidelines for safe interaction with certain in-vehicle distractions such as smartwatches, advanced driving assistance systems, and pets are insufficient or inconsistent across jurisdictions. The rules concerning some distraction types are ambiguous and contain uncertainties. Furthermore, general rules involving distracted driving such as those related to careless driving were found to lack specificity. The results of this investigation provide guidance for policymakers worldwide in developing road rules for distracted driving and the need to change the approach to a more holistic management of distractions.

Distracted driving is a traffic safety issue worldwide. While the development of comprehensive information and road rules about distracted driving by governments is essential to address the issue, there is evidence showing that existing road rules and information may not always deter drivers from engaging in distractions while driving. Therefore, this study explored drivers’ views on government information and road rules concerning distracted driving, aiming to understand how these rules and information have shaped drivers’ perceptions and behaviour towards distractions. Interviews (n = 35) were conducted with Queensland drivers aged between 21 and 70 years and a thematic analysis was used to explore the data. Based on the findings, government information on distracted driving was believed by participants to be incomprehensive, not effectively communicated, and focused on mobile phone use. Road rules and enforcement measures often prioritise mobile phone use and contain grey areas that may confuse drivers about legal and illegal distractions. The results showed that the perceived risk of distractions varies among drivers, with some distractions not being considered as risky as other behaviours (e.g., using a smartwatch). Findings showed that government practices are believed to influence drivers’ perceptions and behaviour about distracted driving, encouraging an inaccurate perception about driver safety and with the potential to prompt drivers to engage with distracting behaviours without knowledge nor consideration of the risks. The results of this study offer important insights for policymakers in developing and disseminating comprehensive information and road rules for distracted driving.

The study applied the Theory of Planned Behaviour (TPB) to explore motorcycle riders’ underlying behavioural, normative, and control beliefs towards Advanced Rider Assistance Systems (ARAS). Each belief was explored in terms of three categories of technologies, (i) advanced technologies that help riders manage riding according to situations and conditions, (ii) advanced technologies that help riders to stop, and (iii) advanced technologies that help riders to corner. Eight focus groups were conducted with 39 motorcycle riders (Mage = 44.54 years, 27 males) who resided in Australia. First, participants completed a short online questionnaire which asked demographic information (e.g., age, gender, riding experience), before taking part in a 50-minute semi-structured online focus group. Participants’ knowledge of ARAS differed depending on the type of technology, with most participants reporting good to excellent knowledge of cruise control and standard anti-lock braking system (ABS) and a poor to fair understanding of selectable riding modes and cornering ABS. For behavioural beliefs, two common advantages reported for all three categories of technologies were safety and that the technologies would benefit new riders or riders with less experience. The three common disadvantages included concerns over riders’ reliance on the technologies, cost, and loss of skill or false sense of security. For normative beliefs, participants reported that their loved ones (i.e., partner, family, and friends) would approve of them using these technologies, with participants perceiving that ‘purists’ (i.e., riders who prefer to ride traditional motorcycles) would disapprove. For control beliefs, cost, lack of information on the safety of advanced technologies, and not being able to switch off systems were reported as barriers to use. Lowering insurance premiums, education/test rides, technologies as selectable options, and availability, were all identified as factors that would encourage use of ARAS. By providing information about ARAS, riders will become more informed about ARAS, which may enhance trust and user acceptance. Additionally, ongoing research and development are essential to ensure the evaluation and improvement of ARAS and mitigate any unintended consequences.

Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; Mage = 49.34 years) who held a current driver's licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads.

Decades of research and development to address young drivers’ over-involvement in road crashes has led to critical improvements in understanding and interventions, yet has mostly focused on young drivers or their parents. Driving instructors have a key role in the young driver safety system, yet substantially less attention has been paid to their accreditation and professional development specific to teaching young drivers. Neurobiological development during mid-to-late adolescence influences how young people drive and the inflated risks they face relative to adult learners. In this paper, we discuss several of our research studies and supporting literature to argue that: (a) adept higher-order professional instruction can improve young driver safety behaviour in ways that can reduce crashes; (b) instructors are willing to be upskilled in young driver specific training needs; and (c) young drivers currently lack adequate training in new motor vehicle features that can enhance their safety, as do many adults, creating further demand on the driver-training industry. Given this body of work, we argue that the field has sufficiently progressed to know both why and how we should innovate and transform the driver training industry in ways that will improve young driver safety. However, we also note current developments appear to be working against rather than towards improvements. Therefore, there is a need to prioritise and implement change, and to harness advantages of emerging technologies. Professional development opportunities for driving instructors not only have potential to benefit the industry and young drivers, but also parents; therefore, boosting traffic safety culture across the community.

Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.

Advanced Driver Assistance Systems (ADAS) have shown substantial potential to increase road safety. To guarantee a growing presence and correct use of ADAS, road safety initiatives such as driver education and programs to promote safer vehicles need to be oriented towards the specific needs of individual drivers. The aim of this study was to explore a sample of Australian drivers’ rationale for driving a vehicle with ADAS and the strategies that they use when learning to operate the functions of ADAS. Semi-structured interviews were conducted with 48 Australian drivers aged between 19 and 78 years (M = 41.54, SD = 16.32; 25 females). The results were organised into four themes: (i) reasons for driving/purchasing a car with ADAS, (ii) systems influencing driver purchasing systems, (iii) strategies used by drivers when learning about ADAS, and (iv) perceptions regarding the importance of learning and training to use ADAS. Acquisition of vehicles with ADAS frequently occurred because of the perceived safety benefits of specific systems (e.g., rear parking sensor or in-vehicle information system). Additionally, some participants did not highlight the safety benefits as the main reason for driving/purchasing a car with ADAS. Indeed, some participants mentioned that ADAS acquisition was not a reasoned process but rather an opportunistic decision when upgrading their vehicles, mainly because these systems were already included in a vehicle. Regarding education, it was found that participants were using trial-and-error as their main approach to learning about the capabilities of ADAS and official sources of knowledge about the system were rarely consulted. On a positive note, participants recognised the importance of learning to use ADAS systems correctly, showing that there is demand for effective and engaging driver education.

Fully automated vehicles (FAVs) have the potential to improve road safety and reduce traffic congestion and emissions. Most studies of acceptance of FAVs have focused on motor vehicle users, largely ignoring other road users, such as cyclists. This study investigates the factors that influence cyclists’ receptivity towards sharing roads with FAVs and their behavioural intentions in interactions with FAVs. The online survey collected information on participant demographics (e.g. age, gender, crash experience), self-reported on-road cycling behaviours (e.g. violations, errors, positive behaviours) and their receptivity towards sharing roads with FAVs (e.g. attitude, social norms, trust). Three typical cyclist-vehicle interaction scenarios were presented to test the cyclists’ intention to engage in self-protective behaviours (e.g. giving a hand signal, giving way or moving over) during the interaction with a FAV. Three hundred and fourteen Australian adults (106 females vs 208 males) who had ridden a bicycle at least once in the past year completed the survey. The results show that older cyclists and male cyclists had a lower receptivity towards sharing roads with FAVs than younger cyclists and female cyclists, respectively. Cyclists who reported being involved in a bicycle crash in the last two years and those who reported committing more errors on roads were more willing to share roads with FAVs. Cyclists who had a higher propensity to risky behaviours and positive behaviours were less likely to take intended self-protective behaviours during interaction with FAVs. Findings of the study provide some insights from the cyclist's perspective to facilitate the development and implementation of automated vehicles.

Automated vehicles have started to be integrated into the road transportation system and operate in a mixed traffic environment. To ensure a smooth and successful integration, it is vital to have a good understanding of the human factor challenges involved in the process, especially the issues related to other road users who will share roads with automated vehicles. The study focuses on conventional vehicle drivers’ acceptance of and interaction with fully automated vehicles (FAV). An online survey with experimental scenarios showing an FAV's lane-changing intention was designed to test the interaction responses of participants. The survey also collected the participants’ demographic information (e.g., age, gender, driving experience), self-reported general driving behaviours (e.g., errors, lapses and violations), past benchmark behaviour in the same situation and their acceptance of FAVs. The study recruited 838 participants in total, comprising 465 participants from Australia (216 males vs. 249 females) and 373 participants from China (172 males vs. 201 females). Ordered probit models were developed to predict three types of behavioural responses of drivers in the lane-changing scenario, i.e., positive, disregardful and aggressive responses. The results showed that older drivers, females, and drivers who had less driving experience were more likely to adopt positive interactions with FAVs than their counterparts. Drivers who reported frequent risky driving behaviours (e.g., aggressions, lapses and errors) were less likely to report positive interaction but more likely to report disregardful and aggressive interactions. Drivers reporting more positive/favourable attitudes and a higher trust toward FAVs demonstrated a higher possibility of positive interaction, and those with higher perceived behaviour control were more likely to restrain disregardful interaction. The study helps to form a greater understanding of conventional vehicle drivers’ perception of FAVs and the underlying factors that may influence their interaction behavioural tendency.

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users’ perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users’ intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers’ willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants’ data donation profile was also measured to assess the influence of individuals’ social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI. Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

Artificial intelligence (AI) chatbots are set to be the defining technology of the next decade due to their ability to increase human capability at a low cost. However, more research is required to assess individuals' behavioural intentions to use this technology when it becomes publicly available. This study applied an extended Technology Acceptance Model (TAM), with additional predictors of trust and privacy concerns, to assess individuals' behavioural intentions to use AI chatbots across three industries: mental health care, online shopping, and online banking. These services were selected due to the current popularity of regular chatbots in these fields. Participants (N=360, 202 females) aged between 17 and 85 years (M=38.17, SD=17.66) completed a 71-item online, cross-sectional survey. As hypothesised, perceived usefulness and trust were significant positive predictors of behavioural intentions across all three behaviours. However, the influence of the perceived ease of use and privacy concerns on behavioural intentions differed across the three behaviours. These findings highlight that the combination of predictors within the extended TAM have different influences on behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking. This research contributes to the literature by demonstrating that the influence of the variables in one field cannot be generalised across all uses of AI chatbots.

This research aimed to examine how personal car users are informed about Advanced Driver Assistance Systems (ADAS) in Australia, and the extent to which information received at point of sale influenced the level of understanding and adaptation of ADAS. Further, this study applied the Technology Acceptance Model (TAM) to assess drivers’ intentions to use ADAS within the next month. Participants were required to have purchased a new or second-hand vehicle within the past five years and which had at least one of the following five ADAS: autonomous emergency braking, lane departure warning, adaptive cruise control, blind spot monitoring, and rear cross traffic alert. Participants (N = 217, Mage = 47.87 years, 149 male) completed a 20-minute online questionnaire. The findings revealed that 122 (56%) participants reported not seeking out any information about ADAS prior to purchasing their vehicle. Further, the most reported approaches used by participants to learn about their vehicle's ADAS were through the owner's manual (n = 121, 55%) and via trial and error (n = 117, 54%). It was also found that total time spent with the salesperson in explaining ADAS features did not influence drivers’ level of understanding of ADAS or number of days to adapt to the ADAS in their vehicle. However, and consistent with the TAM, perceived usefulness and perceived ease of use were significant positive predictors of intentions to use ADAS within the next month. It was also found that participants who learnt about these technologies either via reading the owner's manual, trial and error, or a verbal explanation at point of sale appeared to be more frequently classified as reporting higher acceptance of ADAS than those participants who did not use those learning methods. Given that technology in vehicles will continue to advance, it is vital that more work is conducted to educate drivers about the functionality of ADAS. Further, it is also important to communicate where buyers of personal cars can go and find accurate and reliable information about ADAS.

Intentionally blocking the path of fully automated vehicles is an important dimension of pedestrians’ receptivity towards these vehicles. The monetary value of this behaviour can be obtained by asking pedestrians about their perception of the “fine” for blocking the path of a fully automated vehicle. Econometric modelling of the reported fine can shed more light on factors influencing pedestrians’ receptivity towards fully automated vehicles. However, development of such an econometric model is not straightforward due to the unique characteristics of the dependent variable: it has two fundamentally different states; it is right-truncated; and it may be fat-tailed. Despite fairly extensive methodological advancements in econometric modelling of pedestrian behaviour, there is no model that can adequately explain these characteristics. While a beta distribution in a hurdle setting has the potential to address the above complexities, its applicability in dealing with limited dependent variables in transport applications has remained, by and large, unexplored. This study aims to fill this gap by developing a new beta hurdle regression model that systematically considers the dual-state of a right-truncated dependent variable representing the fine associated with intentionally blocking a fully automated vehicle. The hypothesized model is empirically tested using data obtained from a survey administered in Queensland, Australia, and the results are compared with truncated lognormal, and truncated lognormal hurdle regression models. Results indicate that the hurdle models are superior to the non-hurdle model. The beta variant of the hurdle model provides a better statistical fit for the data that are near their right limit. In addition, parametrizing the variance of the beta distribution captures the additional heterogeneity in the data. Age, gender, education level, violations, attitudes, behaviours that appease social interactions, and perceived ease or difficulty of interacting with fully automated vehicles influence the likelihood and/or the propensity of the fine and thus are associated with the perceived monetary value of intentionally blocking the path of a fully automated vehicle.

Adoption of Automated Vehicles (AVs) within transport networks relies on the technology acceptance of not only AV users, but also other road users such as pedestrians. However, previous research has mostly focused on user acceptance of AVs and the receptivity of pedestrians towards AVs has been largely unexplored. This study aims to fill this gap by applying the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate pedestrians’ intentions to cross a road in front of a fully AV. To achieve this goal, a 20-minute online questionnaire was administered in Australia and data were collected from a total of 485 participants (average age = 35.35 years, 51.5% female). Bivariate correlation analysis and hierarchical regression models were then applied on the data to investigate the association between pedestrian attributes and their behavioural intentions. The findings revealed that the TPB and the UTAUT explained 46% and 43% of the variance in intentions to cross a road in front of a fully AV, respectively, with perceived behavioural control (PBC) and subjective/social norms the most significant unique predictors of intentions within the TPB and UTAUT, respectively. The TAM, however, only explained 35% of the variance in intentions to cross a road in front of a fully AV. When added into Step 2 of the hierarchical regression, age accounted for additional variance above the TAM predictors, indicating that younger participants reported higher intentions to cross a road in front of a fully AV than older participants. Age was not a significant predictor of intentions when entered with the predictors of the TPB and UTAUT. This study provides support for the use of these theoretical models to understand pedestrians’ acceptance of AVs.

Driver education and training are longstanding initiatives for novices to attain a driver license. More recently however, we demand more of such initiatives, expecting they can better prepare young novices to be safer in the early months and years of independent driving. In this chapter, we explore historical and current trends and evidence for these initiatives, both one-on-one and group-based initiatives, in the context of an established theoretical best-practice framework, the Goals for Driver Education. Beyond this, we highlight current lack of attention to driver education and training needs with current advances in driver support technologies in vehicles—both for novices but also experienced drivers. Moreover, we acknowledge the rapidly shifting climate of change towards increased vehicle automation and more livable cities. We argue the need to move beyond the current generalization that most driver education and training efforts are ineffective, to promote more nuanced successful approaches, and ways to best maximize the value of driver education and training to increase driver safety in a changing world.