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.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.     

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。     

Kano model of autonomous driving user acceptance according to driver characteristics: A survey study

The development of advanced technology has revolutionized human life. In this regard, autonomous driving, a core technology currently being developed, is changing rapidly. In addition to improving technology, the acceptance of technology users must be secured. Most relevant studies conducted hitherto have involved evaluation using acceptance elements defined based on the technology acceptance model and the unified theory of acceptance and use of technology. In this study, 21 elements associated with the acceptance of autonomous driving are defined. The Kano model is used to classify the acceptance elements into five attributes and to propose guidelines for improving acceptance. Driver characteristics are classified based on four human factors, which are used to investigate differences in acceptance between groups. A Google survey and fieldwork were completed by 187 participants. Contrary to previous studies, no significant gender differences are observed in the current study. In terms of age, many obstacles are encountered in securing autonomous driving acceptance from the elderly driver group. Additionally, a more conservative tendency is indicated by people with more driving experience. The results of this study reveal important points for identifying elements that hinder future sustainability and commercialization of autonomous driving, thereby facilitating its further technological development.     

Insights into the effectiveness of messaging promoting intentions to use connected vehicle technology

Connected vehicles offer considerable promise for reducing congestion, pollution, and crashes. Nonetheless, less than a third of road users are aware of the potential for connected vehicles to transform transport systems. This study examined the effectiveness of messaging aimed to increase the public’s knowledge of connected vehicle technologies using a short, animated video and its effects as assessed via a survey. Participants were assigned to either a control group, who were not exposed to the messaging, or an intervention group, who were exposed to the messaging, within a pre-post design. Participants (in the intervention group) answered questions about their knowledge of and experience with connected vehicle technologies prior to seeing the messaging and again following being exposed to such messaging together with their intentions to use these technologies in the future. As a theoretically informed investigation, the Theory of Planned Behaviour (TPB) constructs of attitudes, subjective norms, and perceived behavioural control were also measured. The results showed that the messaging increased knowledge about and intention to use connected vehicle technologies. Furthermore, changes in how the TPB constructs predicted intentions were found between the control and intervention groups which may help to explain how the messaging influenced participants’ intentions to use such technology in the future.     

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.     

Effects of traffic context on eHMI icon comprehension

External human–machine-interfaces (eHMIs) might support the interaction between automated vehicles and pedestrians. The messages conveyed by eHMIs need to be understood quickly and correctly by their addressees. If implemented in the future, pedestrians will repeatedly encounter eHMIs in situations that feature different traffic context. So far, little is known about the influence of contextual cues like regulatory elements or (presumed) model behavior of fellow road users on the comprehension of eHMIs. In order to investigate possible effects of such contextual cues on comprehension, we conducted a picture-based online study among German residents (N = 175). Participants repeatedly interpreted three eHMI icons (“you can cross”, “do not cross”, and “pedestrian detected”) either without any context (control group) or within varying degrees of relevant context (experimental group). Context facilitated comprehensibility in terms of accuracy and subjective certainty. Relevant context was especially beneficial at first encounter. As soon as an icon’s meaning was internalized, the necessity of relevant context decreased. The effect of context should therefore be considered in future eHMI research as real-world comprehension might be underestimated otherwise.