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.     

Complex and underreported? A study into the prevalence of suicide by motor vehicle in the state of Queensland

While suicide is recognised as one of the leading causes of death in Australia, it is often presumed that fatal road crashes are primarily accidental in nature. However, international research has indicated that deliberate attempts at self-harm while driving may account for between 1.1% and 7.4% of road crashes. Despite the personal and financial costs associated with such events, few studies have examined the extent of motor vehicle suicides (MVS) or the circumstances surrounding such incidents in Australia. Given this, the current study reviewed coronial, police and toxicology findings of 762 motor vehicle fatalities in Queensland (Australia) during the period 2011 to 2015 to determine the frequency of confirmed cases and explore the prevalence (and characteristics) of possible MVS that remain undetected. In total, 22 confirmed cases were identified, and such events shared significant similarities with 14 possible events, including evidence of recent emotional upset, adversity and mental health diagnoses. In contrast, differences between the groups were limited to confirmed cases being more likely to involve males and contain a suicide note or evidence of past suicidality, while possible cases were more likely to involve a multiple vehicle impact with a truck. Importantly, crucial information regarding the deceased’s psychological state and life circumstances (e.g., psychological autopsy) was not reported in many cases. Corresponding qualitative analysis of the coroners final determination (between the two groups) revealed the occurrence of the word suicide to occur seven times (31.8%) among confirmed cases and five times (35.7%) for possible casers. Furthermore, the coroners final determination of intent to suicide was open or undetermined in six of the possible cases (43%), which further suggests a reluctance (or procedural uncertainty) to categorically identify MVS. The paper further reviews the main findings, illuminates core challenges associated with identifying MVS and proposes a need for future investigations to incorporate a more standardised and evidence-based approach in order to effectively identify such events.     

A comparison of motivational and informational contexts for improving eco-driving performance

Eco-driving, the practice of operating a vehicle with environmentally friendly objectives, has been the focus of an increasing number of driving studies over the past decade, as new forms of vehicle automation and more driver-centric feedback continually emerge. Common benefits range from reducing carbon footprints and emissions to better fuel economy. Studies have also examined the effectiveness of in-vehicle assistance devices and training or education programs for commercial and passenger vehicle operators. Eco-driving strategies in general show significantly smaller impacts in the field relative to their laboratory counterparts. This study further builds on this past work by comparing and disentangling the effects from motivational contexts, for example a competition with prizes, with the effects of informational context, such as tutorials for drivers. To investigate this comparison, laboratory experiments are conducted using a commercial vehicle simulator. The results suggest that motivational contexts are more effective at encouraging eco-driving relative to informational contexts. Furthermore, incentives within competitive contexts had similar positive impacts on reducing carbon dioxide emissions and improving fuel efficiency as experiencing a tutorial on eco-driving. One possible explanation is that drivers have gained knowledge on how to operate vehicles with an eco-driving objective through their driving experience. This result further suggests that with respect to effectively encouraging eco-driving, targeting experienced drivers through a competition is better than presenting information or a tutorial.     

Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics

With rapid advancement in cellphones and intelligent in-vehicle technologies along with driver’s inclination to multitasking, crashes due to distracted driving had become a growing safety concern in our road network. Some previous studies attempted to detect distracted driving behaviors in real-time to mitigate their adverse consequences. However, these studies mainly focused on detecting either visual or cognitive distractions only, while most of the real-life distracting tasks involve driver’s visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, although current vehicles are not commonly equipped with technologies to acquire such data. Also those data are comparatively difficult to acquire in real-time during traffic monitoring operations. To address the above issues, this study focused on developing algorithms for detecting distraction tasks that involve simultaneous visual, cognitive, and physical workload using only vehicle dynamics data. Specifically, algorithms were developed to detect driving behaviors under two distraction tasks – texting and eating. Experiment was designed to include the two distracted driving scenarios and a control with multiple runs for each. A medium fidelity driving simulator was used for acquiring vehicle dynamics data for each scenario and each run. Several data mining techniques were explored in this study to investigate their performance in detecting distraction. Among them, the performance of two linear (linear discriminant analysis and logistic regression) and two nonlinear models (support vector machines and random forests) is reported in this article. Random forests algorithms had the best performance, which detected texting and eating distraction with an accuracy of 85.38% and 81.26%, respectively. This study may provide useful guidance to successful development and implementation of distracted driver detection algorithms in connected vehicle environment, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles.     

Predicting e-bike users’ intention to run the red light: An application and extension of the theory of planned behavior

Electric bike (e-bike) users in China have a high red light running (RLR) rate, contributing to a large number of accidents. This paper aims to examine the psychological motivation of e-bike users’ RLR intentions. A survey questionnaire was designed employing the construct of theory of planned behavior (TPB). The survey was performed in Chengdu, China in November 2016. We found that users older than 40 identify themselves as more cautious riders. Younger riders have higher intention to run the red light. E-bike users with car drivers’ licenses regard running the red light as a more difficult task to perform, and regard this behavior as more morally wrong. Hierarchical regression was used to analyze the data. The results showed that demographic variables (age, marriage status, and college degree), TPB variables (attitude and perceived behavioral control) and extended variables (moral norm and self-identity) are significant predictors for the intention of RLR behavior. The results could provide reference for designing more effective interventions and safety education programs for reducing e-bike users’ RLR rate.     

Does surrounding traffic benefit from an assisted driver with traffic light assistance system?

Traffic light assistance systems enable drivers more energy and time efficient driving behavior at signalized intersections. However, most vehicles will not be equipped with such systems in the next years. These unequipped vehicles’ drivers (UVDs) may benefit from assisted drivers, if they would adapt their behavior. This paper outlines how UVDs (N = 60) interpreted and reacted to a driver with traffic light assistance system. We used a multi-driver simulator with three drivers driving in a car-following scenario. The lead driver was not a participant, but a confederate who was followed by two UVDs. The confederate was apparently equipped either with or without a traffic light assistance system. The traffic light assistance system consisted of two functionalities: a Green Light Optimal Speed Advisory and a start-up assistance system with two different parametrizations. These functionalities aimed at preventing unnecessary changes in speed and reducing the start-up lost time after signal change. The results showed that UVDs benefited from the driving behavior of the confederate with traffic light assistance system. However, the assisted driving behavior was hardly understood and partly rated as aversive by the UVDs. We discuss how to enhance behavioral adaptation of UVDs. We also outline which negative consequences may result from encounters of driver with systems and UVDs. We assume that how UVDs react towards drivers with systems may be one factor contributing to a successful launch of such systems.