Driving without the brain? Effects of value predispositions,media attention,and science knowledge on public willingness to use driverless cars in Singapore

This study employs the cognitive miser model and science literacy model as theoretical frameworks to investigate motivations behind public willingness to use driverless cars in Singapore. Findings from a large-scale survey of 1,006 adult Singaporeans indicate that public willingness to use driverless cars was strongly related to value predispositions, especially affect. Aside from having a direct relationship with willingness to use driverless cars, this study found that affect also had an indirect relationship with willingness to use driverless cars via benefit perceptions. Meanwhile, findings failed to reveal significant relationships between two types of science knowledge and willingness to use driverless cars. The findings shed light on the current public opinion on driverless cars in Singapore and lend support to the cognitive miser model over the scientific literacy model. Theoretical and practical implications are discussed.     

User acceptance of automated public transport: Valence of an autonomous minibus experience

Autonomous driving is receiving increasing attention in the automotive industry as well as in public transport. However, it is still unclear whether users are willing to use automated public transportation at all. In order to answer this and other questions, the transport company of the city of Mainz, Germany, tested the autonomous minibus EMMA (Elektro-Mobilität Mainz Autonom) on a 600-meter-long test track in public space. The study presented here was conducted with the aim of exploring crucial determinants for the use of an autonomous minibus. On the basis of established acceptance models, a questionnaire was developed, which was completed in a field survey by a total of 942 participants before or after their journey with the minibus. Autonomous vehicles in public transport in general and the minibus in particular were evaluated positively by the majority of respondents. Above all, participants judged safety and environmental friendliness of the minibus as important. Participants who completed the questionnaire after their first trip with EMMA provided higher ratings of acceptance than those who had not travelled on the bus. Performance expectancy was the most important predictor for both acceptance of automated public transport in general and acceptance of the minibus EMMA. However, the experienced valence of the ride, in terms of how pleasant or unpleasant passengers experienced the first trip with the minibus, also affected acceptance of the minibus. This suggests a role of valence on intention-to-use, which has hardly been considered in previous theories and studies.     

Behavioral changes to repeated takeovers in automated driving: The drivers’ ability to transfer knowledge and the effects of takeover request process

Only a couple of studies evaluated whether drivers of automated vehicles change their takeover behavior when they experience takeover requests repeatedly. Even less evidence was accumulated regarding the question whether drivers are able to transfer learned behavior to takeover situations with varying visibility characteristics and whether drivers’ takeover behavior depends on the takeover process in these situations. This paper therefore examines three research questions. First, it assesses how drivers change their behavior with the repeated experience of a takeover situation with the same visibility (fog or no fog). Second, it tests whether drivers can transfer their learned takeover behavior from a takeover situation with high or low visibility to the same takeover situation with different visibility conditions. Third, it assesses whether drivers’ takeover behavior and their experience of the situation differ between a one-step and a two-step takeover request process. Forty participants experienced a takeover situation three times. Experimental trials varied between-subjects concerning the permanent presence or absence of fog in the adaptation condition, the change of visibility conditions from fog to no fog or vice versa in the transfer condition, and the design of the takeover process with one-step or two-steps. Dependent variables included participants’ takeover time, minimum time-to-collision (TTC_min) with the construction site, deceleration and maximum steering behavior, and their ratings of criticality of the driving situation and perceived effort. Results show that participants adapted their deceleration behavior when repeatedly experiencing a takeover situation with the same visibility characteristics (adaptation condition). Changing these characteristics (transfer condition) lead to increased minimum TTCs, criticality and perceived effort ratings. In general, participants were able to maintain their takeover behavior in takeover situations with varying visibility characteristics indicating that they can transfer their takeover behavior across situations. Finally, the two-step takeover request process was associated with longer takeover times. However, minimum TTCs were larger and maximum steering movements and criticality ratings were lower compared to the one-step process. We conclude that drivers transfer their behavior across takeover situations. However, this performance comes at higher costs in terms of perceived effort and criticality. In addition, two-step takeover request processes should be favored over one-step processes when designing takeover requests. Future studies should examine the validity of the results in various takeover situations and on-the-road studies.     

Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles

Guided by the Theory of Planned Behaviour (TPB), this study examined the beliefs underpinning, and feasibility of the factors in predicting, individuals’ intentions to use a conditional (Level 3) automated vehicle (AV) and a full (Level 5) AV. Australian drivers (N = 505) aged 17–81 years (Mean age = 33.69, SD = 18.79) were recruited and completed a 20 min online survey which featured both quantitative and qualitative items. For the quantitative data, two linear regressions revealed that the TPB standard constructs of attitudes, subjective norm, and perceived behavioural control (PBC) accounted for 66% of the variance in intentions to use a conditional AV and 68% of the variance in intentions to use a full AV. Of the TPB constructs, attitudes and subjective norms were significant positive predictors of future intentions to use conditional and full AVs. For the qualitative data, some differences emerged for the underlying behavioural beliefs that underpinned intentions to use conditional and full AVs. For example, having beliefs about control over the conditional AV was identified by many participants as an advantage, while not being in full control of the full AV was identified as a disadvantage. For underlying control beliefs, participants identified similar barriers for both vehicle types, including; high costs, lack of trust, lack of control over the vehicle, lack of current legislation to support the mainstream introduction of these vehicles, and concerns of safety for self and for other road users when operating AVs. Overall, these findings provide some support for applying the TPB to understand drivers’ intended use of AVs. However, while the current study showed that the constructs of attitudes and subjective norms might reflect intended use of AVs, more research is required to further examine the role of PBC. Additionally, the findings provide initial insights into the underlying behavioural and control beliefs that may motivate drivers to use AVs and highlight the similarities and differences in drivers’ perceptions towards two levels of vehicle automation.     

SAD as a mathematical assistant—how should we go from here to there?

The System for Automated Deduction (SAD) is developed in the framework of the Evidence Algorithm research project and is intended for automated processing of mathematical texts. The SAD system works on three levels of reasoning: (a) the level of text presentation where proofs are written in a formal natural-like language for subsequent verification; (b) the level of foreground reasoning where a particular theorem proving problem is simplified and decomposed; (c) the level of background deduction where exhaustive combinatorial inference search in classical first-order logic is applied to prove end subgoals.

We present an overview of SAD describing the ideas behind the project, the system's design, and the process of problem formalization in the fashion of SAD. We show that the choice of classical first-order logic as the background logic of SAD is not too restrictive. For example, we can handle binders like Σ or lim without resort to second order or to a full-powered set theory. We illustrate our approach with a series of examples, in particular, with the classical problem .     

The plausibility of using unmanned aerial vehicles as a serious game for dealing with attention deficit-hyperactivity disorder

Attention Deficit-Hyperactivity Disorder (ADHD) is considered a mental disease that affects an estimated 5% of the world’s youth population. Brain-Computer Interfaces (BCIs) such as Electroencephalograms (EEGs) and functional Magnetic Resonance Imaging (fMRI) have been used to study and treat this mental disease. This paper shows the plausibility of using Unmanned Aerial Vehicles (UAVs) as a serious game for therapeutic purposes through a systematic review of the current state of UAVs and how they have been coupled to BCIs in order to control them through mental commands. Also, challenges and research opportunities from a usability approach related to the game design, interaction design, and cognitive rehabilitation are identified and described. We hope that the research opportunities identified in this paper can be useful to guide future research and gain insight into how to solve challenges related to the use of Unmanned Aerial Vehicles in cognitive therapies.     

Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.     

Take-over requests in highly automated driving: A crowdsourcing survey on auditory,vibrotactile, and visual displays

An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in low-urgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens’ power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle- and high-income countries. In summary, this international survey showed that people’s preferences for TOR types in highly automated driving depend on the urgency of the situation.     

Continuous measurement of attachment behavior: A multimodal view of the strange situation procedure

Infant attachment is a critical indicator of healthy infant social-emotional functioning, which is typically measured using the gold-standard Strange Situation Procedure (SSP). However, expert-based attachment classifications from the SSP are time-intensive (with respect both to expert training and rating), and do not provide an objective, continuous record of infant behavior. To continuously quantify predictors of key attachment behaviors and dimensions, multimodal movement and audio data were collected during the SSP. Forty-nine 1-year-olds and their mothers participated in the SSP and were tracked in three-dimensional space using five synchronized Kinect sensors; LENA recordings were used to quantify crying duration. Theoretically-informed multimodal measures of attachment-related behavior (e.g., dyadic contact duration, infant velocity of approach toward the mother, and infant crying) were used to predict expert rating scales and dimensional summaries of attachment outcomes. Stepwise regressions identified sets of multimodal objective measures that were significant predictors of eight of nine of the expert ratings of infant attachment behaviors in the SSP’s two reunions. These multimodal measures predicted approximately half of the variance in the summary approach/avoidance and resistance/disorganization attachment dimensions. Incorporating all objective measures as predictors regardless of significance levels, predicted individual ratings within an average of one point on the original Likert scales. The results indicate that relatively inexpensive Kinect and LENA sensors can be harnessed to quantify attachment behavior in a key assessment protocol, suggesting the promise of objective measurement to understanding infant-parent interaction.