Background Automated vehicles are here now, available for everyone, and with that, everyone starts to want one too. But not everyone likes to drive automatically, or at least in this way. How can we personalize automated driving systems (ADS)? One way is by investigation drivers’ personality. For a large-scale simulator experiment, participants have been recruited by means of the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008). Together with a demographics questionnaire, asking about (among others) experience with ADS, some interesting initial results can be presented on the differences between people in relation to ADS experience. Methods Participants were recruited at the Centraal Bureau voor Rijvaardigheidsbewijzen (CBR) and the Algemene Nederlands Wielrijders Bond (ANWB), among others. Requested to fill in an online questionnaire on demographics, manual- and automated driving experience and exposure, and the BFI, participants were categorised in one of the five traits. The initial data from these questionnaires will be presented here. Results A total of 85 participants (52 male) were recruited, aged between 23-66 years (M[SD]=46.0[10.3]), with 43.5% having experienced driving with ADS. A total of 11 participants were classified as Open (7 male), 18 as Conscientious (10 male), 13 as Extravert (8 male), 17 as Agreeable (12 male), and 26 as Neurotic (15 male). Men had driven more with ADS than women (1k-5k versus <1k), and those who had more ADS experience were older, or Agreeable women. Also a significant negative correlation with education and driving experience was found, except for Open drivers. Conclusions Results combined suggest tech-lover stereotype of rich older men favouring ADS. Furthermore, agreeable women drove more with ADS than agreeable men, which appears to be an odd outlier. The same goes for Open drivers, who do not follow the same trend as the other traits in relation to driving experience against education level: due to their intellectual curiosity? More research is needed; therefore, more participants are being recruited for this study. An update will be presented.@en

Background Automated driving systems (ADS) are exponentially increasing in occurrence and autonomy. Although general rules-of-thumb are slowly being adhered to regarding its human occupant—through Human-Machine Interfaces, take-over requests, etc.—different people respond differently to similar things. Currently, individualising ADS is trending, but no research investigated whether or to what extent different types of personality result in different levels of trust in ADS. This exploratory study asked 120 participants from around the world through an online questionnaire about their trust in ADS and assessed their personality, aimed at finding relations between personality traits and levels of trust in ADS. Methods Via an online crowd sourcing tool (Google CrowdSource), education platforms (university student association/notice boards), and social media (e.g., WhatsApp/Facebook), 120 participants from around the world filled out a questionnaire regarding trust in ADS. The survey included questionnaires on demographics, personality (Big Five Inventory; John et al. 1991; 2008), and trust in ADS (based on Jian and colleagues' [2000] questionnaire). Scores regarding level of trust were divided into five categories (very low to very high trust). A correlation analysis was performed for the Big Five Inventory and trust questionnaire scores per demographics variable. Results In total, 120 participants from 20 different countries (83 male, age M=27, SD=10) filled out the questionnaire. 20 participants did not have a driving license, and 68 were student. A moderate correlation was found where females scoring high on conscientiousness and those scoring low on neuroticism scored high on trust. Perhaps more interestingly, several correlations between trust and personality were found to score close to zero, meaning no correlation whatsoever. All demographics combined, openness and extraversion were least correlated to trust. Conclusions Although commonly thought that the average early adopter of automated driving systems are relatively old, wealthy males (see e.g., Hardman et al., 2019), our results were incapable of confirming this stereotype. Instead, automated driving systems appear to be trusted equally, regardless of the users' personality or demographics. Depsite being a relatively small, exploratory study, these results are promising, and should be expanded. Further research should go more in-depth, investigating other criteria of personality, demographics, and/or trust. @en

The paper presents a framework to realise “meaningful human control” over Automated Driving Systems. The framework is based on an original synthesis of the results of the multidisciplinary research project “Meaningful Human Control over Automated Driving Systems” lead by a team of engineers, philosophers, and psychologists at Delft University of the Technology from 2017 to 2021. Meaningful human control aims at protecting safety and reducing responsibility gaps. The framework is based on the core assumption that human persons and institutions, not hardware and software and their algorithms, should remain ultimately—though not necessarily directly—in control of, and thus morally responsible for, the potentially dangerous operation of driving in mixed traffic. We propose an Automated Driving System to be under meaningful human control if it behaves according to the relevant reasons of the relevant human actors (tracking), and that any potentially dangerous event can be related to a human actor (tracing). We operationalise the requirements for meaningful human control through multidisciplinary work in philosophy, behavioural psychology and traffic engineering. The tracking condition is operationalised via a proximal scale of reasons and the tracing condition via an evaluation cascade table. We review the implications and requirements for the behaviour and skills of human actors, in particular related to supervisory control and driver education. We show how the evaluation cascade table can be applied in concrete engineering use cases in combination with the definition of core components to expose deficiencies in traceability, thereby avoiding so-called responsibility gaps. Future research directions are proposed to expand the philosophical framework and use cases, supervisory control and driver education, real-world pilots and institutional embedding@en

Background Investigating personality is commonly performed using the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008). Five different traits can be distinguished using 44 multiple-choice questions, which can be convenient for preselecting participants; for instance for investigating individual differences in driving with automated vehicles. However, high scores on one trait are regularly accompanied with high scores on another. When aiming for unique participants per trait, this can be troublesome and time-consuming. This study provides a MATLAB calculation method solving this issue. Methods Participant selection is made through a selection algorithm. First, questionnaire answers are placed in an Excel file. Then, five lists (one for each category) are generated of the selected participants who have the highest results. Since it is possible that one participant acquires the highest score or the same percentage in different categories, two algorithms are used. The first normalizes the participants’ scores, and the second tracks the highest score of the five categories. Each participant was selected for (only) their best trait, making for the most profound traits for the entire selection. Results The resulting matrix presents five lists of unique participants with their corresponding score on their respective trait. The code works optimally at higher numbers of entries and has no upper boundary. When a participant scores equally high on two (or more) traits, it selects the trait most beneficial for the entire participant pool, so that each trait has the highest possible average. Conclusions Our MATLAB code, designed for selecting the most appropriate participant for each trait based on the BFI, is found to be successful in selecting unique participants for each trait, and accounting for equal scores on traits, preferring the entire participant pool over the individual scores. This code can be used by other researchers aiming to use the BFI as a means of selection criterion. Our code is found to be robust for higher numbers of entries, and quick and easy to use.@en

Automated bus systems are a promising means of future first- and last mile public transport solutions, and can even possibly become a regular part of the public transport network. Therefore, many projects appear throughout Europe to pilot the feasibility of automated bus system implementation on various locations. Keeping up with the rapidly increasing pace in which these pilots appear, this report aimed to provide an overview of past, currently on-going, and concretely planned pilots with automated bus systems in Europe. Via extensive internet searches, exhausting personal networks, and gathering information from other sources, a detailed overview was developed. 118 pilots were found which were characterized by vehicles with predominantly low speeds, low capacities, and short operation routes. The search in itself proved to be difficult due to the often lacking detailed information of pilots, which was argued to be due to most scientific pilots are of recent years, and therefore often still on-going, and have consequentially not published any information yet on their research. Another difficulty arose due to the rapid increase of occurring pilots with automated buses, which leads to the report already being out-of-date as this report is being written. Therefore, this report will be updated early 2021. Currently, the vast majority of automated bus system pilots occur with the presence of a steward on board, due to legislation, technological challenges, as well as passengers requesting them, raising concerns regarding (e.g., economic) efficiency. Although there are a few automated bus systems that actively show efficient operation without on-board stewards, this still appears to be a future development. @en

Automated bus systems are a promising means of future first- and last mile public transport solutions, and can even possibly become a regular part of the public transport network. Therefore, many projects appear throughout Europe to pilot the feasibility of automated bus system implementation on various locations. Keeping up with the rapidly increasing pace in which these pilots appear, this report aimed to provide an overview of past, currently on-going, and concretely planned pilots with automated bus systems in Europe. Via extensive internet searches, exhausting personal networks, and gathering information from other sources, a detailed overview was developed. In the first version, established March 2020, 118 pilots were found which were characterized by vehicles with predominantly low speeds, low capacities, and short operation routes. In this final version, established February 2021, aside from additional information on known pilots, another 13 were found, making a total of 131 pilots throughout Europe. The search in itself proved to be difficult due to the often lacking detailed information of pilots, which was argued to be due to most scientific pilots being of recent years, and therefore often still on-going, and consequentially not having published any information yet on their research. Another difficulty arose due to the rapid increase of occurring pilots with automated buses, which leads to the report already being out-of-date as this report is being written. Therefore, this report was updated early 2021. Meanwhile, the Covid-19 pandemic situation appears a major issue for automated bus systems pilots during the year 2020. The results show that currently the vast majority of automated bus system pilots occur with the presence of a steward on board, due to legislation, technological challenges, as well as passengers requesting them, raising concerns regarding (e.g., economic) efficiency. Although there are a few automated bus systems that actively show efficient operation without on board stewards, this still appears to be a future development. @en

Automated driving systems promise a tremendous amount of benefits. Especially when applied in the domain of public transport, economic and passenger advantages are thought to be manifold. As technology rapidly advances, and projects involving automated buses appear throughout the world, investigating how its users and surrounding road traffic interact with these novel technologies need to advance with a similar pace. However, up to now, a reliable and up-to-date overview of performed, running, and planned projects is lacking. Moreover, little is known about human interaction with automated bus systems, and what is known is not always reported. By means of a systematic review, an overview of the current state-of-the-art knowledge on the interaction between automated bus systems and its interactors is presented. Results of these studies are described and discussed, and implications are being made regarding future policies to be applied in this domain to safeguard safe interaction with automated bus systems.

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Increased on-road testing and market availability of partially automated vehicles (AV) offers researchers and developers the opportunity to evaluate the AV’s performance. The occurrence of new types of accidents involving AV’s has sparked questions in regard to who is actually in control over and responsible for AV control. In this contribution, we suggest a potential discrepancy in AV control with the review of recently documented accidents involving AV’s. The identification of a gap in control is performed using a recently formulated moral philosophical framework of Meaningful Human Control (MHC). This shows a discrepancy between the attribution of responsibility and the ability of a human to fulfil the role assigned to them. While a gap in control is not evident from the viewpoint of operational control, it requires the more intricate concept of MHC to expose it. Recommendations are further made that AV developers and vehicle approval authorities should consider control from a MHC perspective to avoid future gaps in control with the resulting consequences.@en

Automated driving systems (ADS) with partial automation are currently available for the consumer. They are potentially beneficial to traffic flow, fuel consumption, and safety, but human behaviour whilst driving with ADS is poorly understood. Human behaviour is currently expected to lead to dangerous circumstances as ADS could place human drivers ‘out-of-the-loop’ or cause other types of adverse behavioural adaptation. This article introduces the concept of ‘meaningful human control’ to better address the challenges raised by ADS, and presents a new framework of human control over ADS by means of literature-based categorisation. Using standards set by European authorities for driver skills and road rules, this framework offers a unique, quantified perspective into the effects of ADS on human behaviour. One main result is a rapid and inconsistent decrease in required skill- and rule-based behaviour mismatching with the increasing amount of required knowledge-based behaviour. Furthermore, the development of higher levels of automation currently requires different human behaviour than feasible, as a mismatch between supply and demand in terms of behaviour arises. Implications, discrepancies and emerging mismatches this framework elicits are discussed, and recommendations towards future design strategies and research opportunities are made to provide a meaningful transition of human control over ADS.

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Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants’ mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

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The future adoption of automated vehicles poses many challenges, with one of the more important being the preservation of control over vehicles that are no longer (fully) operated by drivers. There is consensus that vehicles should not perform actions that are unacceptable to humans. In this paper, we introduce the concept of Meaningful Human Control (MHC) as a function of a framework of the Automated Driving System (ADS). This framework is constructed through the core components that make up the ADS, primarily considered within the categories of the vehicle and driver. Identification of these components and the chain of control allow traceability of MHC to be performed, and aids vehicle manufacturers, software developers, other vehicle component designers, and vehicle- and driver licensing authorities to address many challenges related to the design and preservation of human control in automated vehicles. Operationalisation of MHC is discussed in the paper including a suggested approach that should aid understanding and the application of the concept. Four application examples are given and recommendations are made in regard to vehicle design, human machine interaction, transition of control, driver training, vehicle approval, and other topics. The framework and presented concept also allow researchers to identify areas to perform more explicit and relevant research and develop models that can be applied to perform projections of future impacts.@en

Truck platooning is a form of vehicle automation and cooperation that is leading the way for cooperative and automated vehicle implementation. However, much is still unknown about the effects and potential dangers of many situations in regard to cooperative control of these platoons. In this contribution, we discuss many of the challenges in regard to full platoon control, we give concepts that can answer some of the questions and make recommendations on how full platoon control should be considered by truck manufactures, ADS software developers and policy makers. A main concept that is applied is that of Meaningful Human Control (MHC). We furthermore consider driver 'reasons', both distal and proximal, to identify correct chains of MHC. We conclude that each part of a system should be responsive to the maximum amount of relevant reasons available and the availability of relevant reasons should be maximized to obtain sufficient MHC.

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Previous research shows that drivers of automated vehicles are likely to engage in visually demanding tasks, causing impaired situation awareness. How mental task demands affect situation awareness is less clear. In a driving simulator experiment, 33 participants completed three 40-min runs in an automated platoon, each run with a different level of mental task demands. Results showed that high task demands (i.e., performing a 2-back task, a working memory task in which participants had to recall a letter, presented two letters ago) induced high self-reported mental demands (71% on the NASA Task Load Index), while participants reported low levels of self-reported task engagement (measured with the Dundee Stress State Questionnaire) in all three task conditions in comparison to the pre-task measurement. Participants’ situation awareness, as measured using a think-out-loud protocol, was affected by mental task demands, with participants being more involved with the mental task itself (i.e., to remember letters) and less likely to comment on situational features (e.g., car, looking, overtaking) when task demands increased. Furthermore, our results shed light on temporal effects, with heart rate decreasing and self-constructed mental models of automation growing in complexity, with run number. It is concluded that mental task demands reduce situation awareness, and that not only type-of-task, but also time-on-task, should be considered in Human Factors research of automated driving.

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Human Factors issues with automated driving systems (ADS) are becoming more apparent with the increasing prevalence of automated vehicles on the public roads. As automated driving demands increased performance of supervisory skills of the driver, rather than vehicle handling skills, a mismatch occurs between the demand and supply of the drivers’ skillset. Therefore, it has been suggested that drivers should at all times have meaningful human control (MHC) over ADS. The basic idea behind MHC is derived from the debate on autonomous weapon systems, and entails three essential components: human operators are (1) making informed, conscious decisions, (2) sufficiently informed about lawfulness of an action and its context, and (3) properly trained, to ensure effective control over the use of ADS. This paper presents definitions, components and potential human roles within ADS, from an interdisciplinary and a MHC perspective. The ideas presented in this paper are valuable to both designers, manufacturers, and road operators, as well as policy makers, driving licensing bodies, and lawyers and insurers, and our future research into these topics will deliver usable results for all stakeholders. @en

Platooning, whereby automated vehicles travel closely together in a group, is attractive in terms of safety and efficiency. However, concerns exist about the psychological state of the platooning driver, who is exempted from direct control, yet remains responsible for monitoring the outside environment to detect potential threats. By means of a driving simulator experiment, we investigated the effects on recorded and self-reported measures of workload and stress for three task-instruction conditions: (1) No Task, in which participants had to monitor the road, (2) Voluntary Task, in which participants could do whatever they wanted, and (3) Detection Task, in which participants had to detect red cars. Twenty-two participants performed three 40-min runs in a constant-speed platoon, one condition per run in counterbalanced order. Contrary to some classic literature suggesting that humans are poor monitors, in the Detection Task condition participants attained a high mean detection rate (94.7%) and a low mean false alarm rate (0.8%). Results of the Dundee Stress State Questionnaire indicated that automated platooning was less distressing in the Voluntary Task than in the Detection Task and No Task conditions. In terms of heart rate variability, the Voluntary Task condition yielded a lower power in the low-frequency range relative to the high-frequency range (LF/HF ratio) than the Detection Task condition. Moreover, a strong time-on-task effect was found, whereby the mean heart rate dropped from the first to the third run. In conclusion, participants are able to remain attentive for a prolonged platooning drive, and the type of monitoring task has effects on the driver's psychological state.@en

Automatically driving platoons of vehicles are a likely candidate for solving many existing issues of road safety and congestion. However, the psychological effects of such technology are yet to be understood. Therefore, by means of a driving simulator experiment, we aimed to assess the psychological effects of driving in a highly automated platoon. The results showed that the type of task had no substantial effect on heart rate and self-reported stress, fatigue, and workload. However, time-on-task substantially reduced participants’ heart rate. @en