A structural equation modeling approach for the acceptance of driverless automated shuttles based on constructs from the Unified Theory of Acceptance and Use of Technology and the Diffusion of Innovation Theory

The present study investigated the attitudes and acceptance of automated shuttles in public transport among 340 individuals physically experiencing the automated shuttle ‘Emily’ from Easymile in a mixed traffic environment on the semi-public EUREF (Europäisches Energieforum) campus in Berlin. Automated vehicle acceptance was modelled as a function of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs performance expectancy, effort expectancy, social influence, and facilitating conditions, the Diffusion of Innovation Theory (DIT) constructs compatibility and trialability, as well as trust and automated shuttle sharing. The results show that after adding the DIT constructs, automated shuttle sharing, and trust to the model, the effect of performance expectancy on behavioural intention was no longer significant. Instead, compatibility with current travel was the strongest predictor of behavioural intention to use automated shuttles. It was further found that individuals who are willing to share rides in an automated shuttle with fellow travelers (i.e., automated shuttle sharing) and who trust automated shuttles (i.e., trust) are more likely to intend to use automated shuttles (i.e., behavioural intention). The highest mean rating was obtained for believing that automated shuttles are easy to use, while the lowest mean rating was obtained for feeling safe inside the automated shuttle without any type of supervision. The analysis revealed a preference for the supervision of the automated shuttle via an external control room to the supervision by a human steward onboard. We recommend future research to investigate the hypothesis that compatibility could serve as an even stronger predictor of the behavioural intention to use automated shuttles in public transport than performance expectancy.     

Passengers’ acceptance and perceptions of risk while riding in an automated vehicle on open,public roads

Automated vehicles are an emerging technology that operate with differing levels of automatic control (SAE levels). The current study explored participants’ acceptance of a conditional (Level 3) automated vehicle (AV) before and after riding as a passenger for 10 min on open, public roads in uncontrolled traffic. Additionally, participants were asked to rate the riskiness (perceived risk) of a variety of vehicle maneuvers, such as turning, accelerating, and braking when approaching an intersection. We predicted that participants would report higher acceptance ratings and lower perceived risk ratings after experiencing the AV compared to pre-trip ratings. Further, we predicted that participants riding in the front-passenger seat would report higher ratings for risk compared to participants sitting in the rear-passenger or rear-driver seats. Sixty participants from South-East Queensland, Australia (aged 21–82 years; M_age = 45.78; 23 female) took part in the study. Compared to pre-trip responses, participants reported statistically higher ratings for acceptance, as well as statistically lower ratings for perceived risk with respect to specific vehicle maneuvers performed while in automated mode. Differences were detected between seats for perceived risk, but these results were less clear. Increases in acceptance and decreases in risk were also detected as the number of false hazards detected by the vehicle increased. Overall, these findings suggest that acceptance towards AVs may increase, and expectations of risk related to AV maneuvers may decrease, after participants have experienced the vehicles firsthand, on an open, public road in an uncontrolled traffic environment.     

Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving

Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, we developed a new data-driven questionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, we conducted a cross-sectional questionnaire study with 725 respondents (351 female, 374 male) ranging from 18 to 96 years. We applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, we identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items; long: 16) and L5 (short: 11, long: 17), respectively. Additionally, we queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. Our findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other questionnaires on AD in different countries around the globe.     

Impressions after an automated mobility experience: An acceptance study

This paper presents people’s impressions after a demonstration of a fully robotized electric vehicle in Rambouillet, a peri-urban area connected with rural sections in France. 155 participants experienced a 6.5 km ride that included driving in a narrow two-way road, negotiating roundabouts, traversing a tunnel and interacting with other users of the road such as vehicles, pedestrians and cyclists. 298 impressions from 114 respondents were collected from a written questionnaire and open interviews. Users’ comments were classified in categories as follows: system capabilities, purpose and benefits, travel destination and encouraging remarks. Major results show that participants felt safe, where only 1.01% of comments denoted apprehension despite the increase of speed (up to 50 km/h) with respect to other studies. Users’ were satisfied with the automated vehicle (AV) performance, where only 3.5% criticized the system, denoting they expected a higher level of autonomy. 22% of users mentioned train stations as useful application (i.e. potential destination) for an automated on-demand mobility service. More than 200 comments were related to travel destinations, pointing towards various scenarios where an AV service could be useful for them. Conclusions show that realistic vehicle speed increases passenger acceptance of automated vehicles, increasing system confidence with respect to previous studies in the field.     

I’m curious,I’m open to it,I test it,I trust it! A focus groups study to understand a-priori trust in automated buses

Trust is regarded as one of the main predictors for adopting automated buses (ABs). However, theories about trust (development) in technology generally vary and an in-depths study about trust in ABs specifically is still outstanding. The present study fills this gap by presenting results from focus group interviews to trust (development) in shared automated buses prior to exposure. The objectives of this study are to contrast participants’ naïve concepts of trust with theory and to identify underlying factors influencing a-priori trust in ABs. Results show that the N = 21 focus group participants use different strategies to familiarise themselves with the new technology of ABs, e.g., comparisons with familiar technologies, fundamental tendencies to approach or avoid, additional information seeking, or anthropomorphisation. These strategies largely support existing theories on trust (development) in technology. Differences between naïve interpretations of trust and its theoretical assumptions were found in focus group debates where more control over technology limited uncertainty and led to more trust. While theories suggest control and trust to be incompatible opposites, participants see control as a way to enhance trust. We provide starting points for further theory development and expansion and stress the importance of explanations in emerging technologies for trust and acceptance building.     

Faster Teaching via POMDP Planning

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision‐theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept‐learning tasks, we show that this technique can accelerate learning relative to baseline performance.     

Defining answer classes using resolution refutation

Resolution theorem proving provides a useful paradigm for the exploration of question answering. A partition of the clauses generated during resolution refutation based on their syntactic structure is presented. The three classes comprising this partition correspond to semantically intuitive types of answers. This work encompasses and expands upon previous work on question answering in a theorem proving paradigm, which began with the association of answers with proofs. A complete, formal definition of what is meant by answer in the context of resolution theorem proving is presented. In this context, clauses that are relevant are all identified as answers, where relevance is determined with respect to a question and knowledge base: any clause descended from the clause form of a negated question is deemed relevant. This definition of relevance is not in and of itself novel; rather, it is the way in which the set of relevant clauses is partitioned that provides the key to interpreting clauses as answers. The three answer classes identified are: specific, generic, and hypothetical. These classes are formally distinguished by the way in which literals in a clause share variables, with class membership based on a property termed the closure of variable sharing of a literal. The results presented provide a foundation for further work by establishing a context-independent logical pragmatics of question answering.     

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

In the near future, automated vehicles (AVs) will enter the urban transport system. This fact will lead to mixed traffic consisting of AVs, human car drivers and vulnerable road users. Since the AV’s passenger no longer has to monitor the driving scene, conventional communication does not exist anymore, which is essential for traffic efficiency and safety. In research, there are plenty of studies focusing on how AVs could communicate with pedestrians. One approach is to use external human-machine interfaces (eHMIs) on the AV’s surface. In contrast to the studies dealing with AV-pedestrian communication, this paper focuses on communication strategies of AVs with drivers of regular vehicles in different road bottleneck scenarios. The eHMI development and design is building on previously defined requirements and on fundamentals of human visual perception. After designing several eHMI drafts, we conducted a user survey with 29 participants resulting in the final eHMI concept. The evaluation of the evolved eHMI was conducted in a driving simulator experiment with 43 participants investigating the AV-human driver interaction at road bottlenecks. The participants were assigned either to the experimental group being faced with the eHMI or to the baseline group without explicit communication. The results show significantly shorter passing times and fewer crashes among the human drivers in the group with the eHMI. Additionally, the paper researches the aftereffects of an automation failure, where the AV first yields the right of way and then changes its strategy and insisted on priority. Experiencing the automation failure is reflected in increased passing times, reduced acceptance ratings and a lower perceived usefulness. In conclusion, especially in unregulated bottleneck scenarios flawless communication via eHMIs increases traffic efficiency and safety.