Impact of interface design on drivers’ behavior in partially automated cars: An on-road study

In partially automated vehicles, the driver and the automated system share control of the vehicle. Consequently, the driver may have to switch between driving and monitoring activities. This can critically impact the driver’s situational awareness. The human–machine interface (HMI) is responsible for efficient collaboration between driver and system. It must keep the driver informed about the status and capabilities of the automated system, so that he or she knows who or what is in charge of the driving. The present study was designed to compare the ability of two HMIs with different information displays to inform the driver about the system’s status and capabilities: a driving-centered HMI that displayed information in a multimodal way, with an exocentric representation of the road scene, and a vehicle-centered HMI that displayed information in a more traditional visual way. The impact of these HMIs on drivers was compared in an on-road study. Drivers’ eye movements and response times for questions asked while driving were measured. Their verbalizations during the test were also transcribed and coded. Results revealed shorter response times for questions on speed with the exocentric and multimodal HMI. The duration and number of fixations on the speedometer were also greater with the driving-centered HMI. The exocentric and multimodal HMI helped drivers understand the functioning of the system, but was more visually distracting than the traditional HMI. Both HMIs caused mode confusions. The use of a multimodal HMI can be beneficial and should be prioritized by designers. The use of auditory feedback to provide information about the level of automation needs to be explored in longitudinal studies.     

Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments

External human machine interfaces (eHMI) might contribute to an enhanced traffic flow and road safety by providing relevant information to surrounding road users. To quantify the effect of eHMI on traffic flow, the majority of studies required participants to indicate their crossing decision in an explicit manner, such as pressing a button. While this approach proved to be efficient, the transfer to real-world behavior is unclear. Here, we propose a more realistic, motion-based approach allowing pedestrians to actually cross the road in front of a vehicle in a virtual reality environment. Participants (N = 51) encountered simulated automated vehicles (AVs) in two scenarios. We investigated the effect of different eHMIs on traffic flow and road safety. Pedestrians’́ body movements were obtained using a motion capturing system with six sensors. Our approach was validated using a two-step procedure. First, we assessed crossing behavior and subjective safety feeling while approaching AVs with and without eHMI. Second, we tested to which extent objective crossing behavior matched self-reported safety feeling. For this purpose, we evaluated if subjective safety feeling can be reliably predicted from actual crossing behavior using a functional data analysis. The proposed motion-based approach proved a valid investigation method for eHMI designs. The results indicated that eHMIs have a beneficial effect on traffic flow and road safety. Regarding traffic flow, participants crossed the road earlier and felt significantly safer when encountering an AV with an eHMI compared to no eHMI. In addition, in situations in which only some of the AVs were equipped with an eHMI, participants’ crossing behavior and safety feeling became more conservative for encounters without eHMI, indicating higher road safety. Further, subjective safety feeling was significantly predicted from actual crossing behavior. These findings highlight that eHMIs are beneficial for pedestrians’ crossing decision, both from an objective and subjective perspective.     

Factors that influence the acceptance of future shared automated vehicles – A focus group study with United Kingdom drivers

The development of Shared Automated Vehicles (SAVs) is well underway to provide mobility as a service (MaaS) and bring benefits such as reduced traffic congestion, reduced reliance on privately owned vehicles and increased independence to non-drivers. To realise the benefits of SAVs, adoption by private vehicle users is crucial. Previous research has shown this subset of users as the least likely to adopt SAVs, and it is not well understood what factors are important to achieve such adoption. The purpose of this study is to obtain an in-depth understanding of attitudes, perceptions and preferences that influence the acceptance of future SAVs for drivers. This paper presents the results from an online asynchronous focus group study with 21 British drivers as participants. From the analysis, Service Quality, Trust and Price Value emerged as the three most prominent factors to understand user acceptance of SAVs. These three main factors may be of prime importance for convincing naïve private car owners to accept high-speed SAVs. Productive use of travel time has been frequently mentioned in previous research as a benefit of vehicle automation but was scarcely mentioned by participants in this study. Shared Space Quality in introduced as an indicator for Service Quality, together with Security and Trusting Co-passengers as two indicators of Trust. Based on the findings, this paper concludes with a conceptual SAV technology acceptance model is introduced, with the results added as extended model predictors to the Unified Theory of Acceptance and Use of Technology (UTAUT2).     

Should older people be considered a homogeneous group when interacting with level 3 automated vehicles?

Exploring the future mobility of older people is imperative for maintaining wellbeing and quality of life in an ageing society. The forthcoming level 3 automated vehicle may potentially benefit older people. In a level 3 automated vehicle, the driver can be completely disengaged from driving while, under some circumstances, being expected to take over the control occasionally. Existing research into older people and level 3 automated vehicles considers older people to be a homogeneous group, but it is not clear if different sub-groups of old people have different performance and perceptions when interacting with automated vehicles. To fill this research gap, a driving simulator investigation was conducted. We adopted a between-subjects experimental design with subgroup of old age as the independent variable. The differences in performance, behaviour, and perception towards level 3 automated vehicles between the younger old group (60–69 years old) and older old group (70 years old and over) was investigated. 15 subjects from the younger old group (mean age = 64.87 years, SD = 3.46 years) and 24 from the older old group (mean age = 75.13 years, SD = 3.35 years) participated in the study. The findings indicate that older people should not be regarded as a homogeneous group when interacting with automated vehicle. Compared to the younger old people, the older old people took over the control of the vehicle more slowly, and their takeover was less stable and more critical. However, both groups exhibited positive perceptions towards level 3 automation, and the of older old people’s perceptions were significantly more positive. This study demonstrated the importance of recognising older people as a heterogeneous group in terms of their performance, capabilities, needs and requirements when interacting with automated vehicles. This may have implications in the design of such systems and also understanding the market for autonomous mobility.     

Behavioural intention to use fully autonomous vehicles: Instrumental,symbolic, and affective motives

Autonomous Vehicles (AVs) have the potential to transform the transportation industry with significant economic, social and environmental benefits. However, the mass deployment of AVs depends on public desire to use them. This study aims to examine the effect of instrumental, symbolic, and affective motives on the behavioural intention to use fully AVs. Based on a survey of 240 U.S. residents, a structural equation modeling analysis was performed. Our results suggest the behavioural intention to use fully AVs depends on fulfilling instrumental (i.e., performance expectancy and hedonic motivation), symbolic (i.e., personal innovativeness and social influence) and affective motives (i.e., trust and performance risk). These results have implication for designing policy interventions to increase the deployment of AVs.     

Do cyclists need HMIs in future automated traffic? An interview study

Cyclists are expected to interact with automated vehicles (AVs) in future traffic, yet we know little about the nature of this interaction and the safety implications of AVs on cyclists. On-bike human–machine interfaces (HMIs) and connecting cyclists to AVs and the road infrastructure may have the potential to enhance the safety of cyclists. This study aimed to identify cyclists’ needs in today’s and future traffic, and explore on-bike HMI functionality and the implications of equipping cyclists with devices to communicate with AVs. Semi-structured interviews were conducted with 15 cyclists in Norway and 15 cyclists in the Netherlands. Thematic analysis was used to identify and contextualise the factors of cyclist-AV interaction and on-bike HMIs. From the analysis, seven themes were identified: Interaction, Bicycles, Culture, Infrastructure, Legislation, AVs, and HMI. These themes are diverse and overlap with factors grouped in sub-themes. The results indicated that the cyclists prefer segregated future infrastructure, and in mixed urban traffic, they need confirmation of detection by AVs. External on-vehicle or on-bike HMIs might be solutions to fulfil the cyclists’ need for recognition. However, the analysis suggested that cyclists are hesitant about being equipped with devices to communicate with AVs: Responsibility for safety should lie with AV technology rather than with cyclists. A device requirement might become a barrier to cycling, as bicycles are traditionally cheap and simple, and additional costs might deter people from choosing cycling as a transport mode. Future studies should investigate user acceptance of on-bike HMIs among cyclists on a larger scale to test the findings’ generalisability, and explore other, perhaps more viable solutions than on-bike HMIs for enhancing AV-cyclist interaction.     

Empirical evidence of bias in public acceptance of autonomous vehicles

This study synthesizes 91 peer-reviewed survey studies examining the public acceptance of Autonomous Vehicles (AVs). The framework of the study is informed by three questions: (1) How well do the collected samples represent the acceptance of the general population? (2) How often does bias exist in measuring public acceptance in AV’s questionnaires? (3) How much bias persists in reporting public acceptance of AV’s research? The findings indicate that (1) people with disabilities and racial minorities are only included in 10% and 20% of the studies, respectively (2) 50% of the studies present their questionnaire, and most are perceived to be biased as a result of systematic errors such as leading questions, missing questions, and suggestive information, and (3) 72% of the studies suffer from the sentiment bias, where the positive tone in the title and abstract is more significant than in the result. This leads to imprecise findings and unrealistic depictions of acceptance of autonomous vehicles by the public. The analysis alerts researchers and practitioners to empirical evidence of bias in public acceptance of autonomous vehicles and recommends preventive actions.     

Research on parking choice behavior of shared autonomous vehicle services by measuring users’ intention of usage

Shared autonomous vehicles (SAVs) are one of the important development directions of smart and green transportation. However, relevant researches are not sufficient at present. The factors influencing the intention to use SAVs and their parking choice behaviors need to be further analyzed. First, in order to better explain, predict, and improve travelers’ intention to use SAVs, the conceptual framework based on technology acceptance model was developed to establish the relationships between the travelers’ intention to use SAVs, social influence of SAVs, attitude toward behavior of SAVs, perceived risk of SAVs, perceived usefulness of SAVs and perceived ease of these use. Then structural equation model (SEM) was established to analyze the relationship between various variables. The results show that the perceived usefulness, behavior attitude, social influence, perceived ease of use, and perceived risk are the main factors that determine the intention to use SAVs. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on intention to use SAVs, with a standardized coefficient of 0.765, followed by behavior attitude (0.732), social influence (0.597), perceived ease of use (0.462) and perceived risk of SAVs (−0.452). In addition, through the study of observed indicator variables ATB2 and BI3, it is found that perceived usefulness, perceived ease of use, social influence, perceived risk, attitude toward behavior, and behavior intention all have an impact on parking behavior. In order to study the specific influencing factors of parking choice behavior, a multinomial logit (MNL) model was established to analyze the relationships between travelers’ parking choice behaviors and the influential factors, which include travelers’ individual characteristics, travel attributes, and parking modes’ attributes by extracting from a questionnaire. The results show that the travel time, travel fees, parking charge, cruising fees, parking time and traffic emission are the main factors that determine travelers’ choices of parking. This paper provides advice for operators of SAVs.     

The Health and Well-Being of Military Drone Operators and Intelligence Analysts: A Systematic Review

The aim of this study was to systematically review the existing research on the health and well-being of military drone operators and intelligence analysts in order to provide an overview of research and identify gaps in this area. Six literature databases and 2 databases containing unclassified military reports were searched for relevant papers produced between January 1996 and May 2016. The search criteria were broad to allow for the identification of all relevant studies on the topic. Fifteen studies met the inclusion criteria; all of which were conducted in the U.S. with the U.S. Air Force personnel. The main sources of occupational stress reported by participants across the studies were operational. The rates of mental health diagnoses, including PTSD, were low, but levels of psychological distress were higher in drone and intelligence operators than in comparison groups. Fatigue emerged as a significant concern. It is important that future studies examine a variety of mental and physical health outcomes. The health and well-being of drone operators and intelligence analysts should be studied not just in the U.S., but also in other countries that are using drones for military purposes.     

交互自然性的心理结构及其影响

本文通过3个研究探究了交互自然性的心理结构及其影响。研究1通过开展字典检索, 文献回顾和专家访谈, 得到了包含有9个条目的初始量表。研究2问卷调查了353名智能网联汽车用户, 探索性因素分析发现了两因素结构 (“通达舒畅”和“随景应人”)。后续分析表明这两个因素对满意度等关键效标有显著独特的预测作用。研究3使用新样本(n = 349) 验证了双因素模型的稳定性, 还发现这两个交互自然性体验维度对推荐意愿、忠诚感等重要变量也有显著预测作用, 此外还发现, 通达舒畅更多受到基本驾驶辅助系统等功能的影响, 而随景应人更多受到交互和智能相关功能的影响。本文进一步讨论了该量表如何用于未来的人机交互研究。