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。     

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.     

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.     

What drives tourists to adopt self-driving cars?

Autonomous vehicles are expected to shape mobility and tourism. This paper introduces an extension to the TAM to better understand the adoption of self-driving cars for tourism purposes. The new model (TAMAT) confirms some under-explored impacts of tourism-related variables, such as Openness to Tourism Usage and Unusual Surroundings, and the Adherence to Conventional Use on the Intention to Use self-driving cars. The research is based on online data collection (n = 646) and applies Covariance-Based Structural Equation Modelling. Findings indicate that the opportunity of using self-driving cars for tourism and unusual environments has a positive impact, while adherence to conventional car use has a negative impact on the intention to use self-driving cars.     

Why would people want to travel more with automated cars?

The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV.We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe).The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes.     

Survey on biometry for cognitive automotive systems

A survey on biometry for cognitive automotive systems is presented in this paper, specially those biometric systems used for high tech security access, law enforcement and/or commercial transactions. In general, biometric systems can be expensive due to the amount of sensors and processing resources involved. Efforts have been made to integrate these systems to vehicles mostly for security purposes and user authentication. Until now, most of the systems do not go beyond using facial and fingerprint data to start the engine or access the car; however, new generations demand more personalization plus the vehicle making decisions based on their physiological characteristics. Although this kind of technology is considered a luxury feature in general, actually it could help users and save their lives. Indeed, biometry is the way to make the human-vehicle relationship happen, whether the biometric devices are embedded inside the vehicle, used as an accessory or wearables. By using as input not only the sensors inside the vehicle but also data from outside, the vehicle could adapt and/or learn new information to make the best possible decision.     

Ethical and economic issues in the use of zero-emission vehicles as a component of an air-pollution mitigation strategy

The air pollution generated by motor vehicles and by static sources is, in certain geographic areas, a very serious problem, a problem that exists because of a failure of the marketplace. To address this marketplace failure, the State of California has mandated that by 2003, 10% of the Light-Duty Vehicle Fleet (LDV) be composed of Zero-Emission Vehicles (ZEVs). However, the policy-making process that was utilized to generate the ZEV mandate was problematic and the resulting ZEV mandate is economically unsound. Moreover, an ethical analysis, based primarily upon the work of John Rawls, suggests that implementation of the California ZEV mandate is—in spite of the wide latitude that ought to be given to policy decision makers—unethical. A more ethical and economically efficient approach to the pollution caused by marketplace failure is one that relies on market incentives and thereby achieves the desired improvement in air quality by appealing both to the self-interest of motorists and to those businesses that are directly or indirectly involved with the automobile industry. Such an approach would take better advantage of the creative forces of the market and improvements in technology over time and avoid the infringements on individual liberty and fairness embodied in the ZEV mandate.     

“They have to be better than human drivers!” Motorcyclists’ and cyclists’ perceptions of autonomous vehicles

Road users and the general population by and large recognise the value of vehicles with automated driving systems and features (otherwise typically known as Autonomous Vehicles (AVs)) in terms of road safety, reduced emissions and convenience, but are still wary of their capability, preferring the ‘comfort zone’ of human operator intervention. Motorcyclists and cyclists conversely, are vulnerable to human fallibility in driving, with the majority of crashes occurring as a consequence of other drivers’ inattention. The transition period associated with the introduction of AVs will require AVs and motorcyclists/cyclists sharing the road for a number of years yet, so we need to understand motorcyclists’/cyclists’ perception of AVs. The question of interest here is whether motorcyclists/cyclists reflect the historical literature in this area by having higher levels of trust for human drivers over AVs, or whether they have higher levels of trust in AVs because it removes the ‘human element’ that has been proven to be particularly dangerous for them. Here we surveyed motorcyclists and cyclists about their trust in human drivers and AVs, and developed a novel suite of questions designed to interrogate the difference between trust in general versus trust as a concept of their own personal safety. Some of the salient outcomes suggest that motorcyclists have medium to low levels of trust for both human drivers and AVs, but are significantly more likely to believe that AVs are safer in terms of their own personal safety, such as prioritising or detecting the rider, compared to human drivers. This relationship varies with age and crash experience. The results here are consistent with the logic that motorcyclists/cyclists have a heightened sense of vulnerability on the road and welcome the introduction of AVs as a way of mitigating personal risk when riding. This insight will be crucial to the subsequent roll-out of AVs in the future.     

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.     

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.