How do familiarity and fatal accidents affect acceptance of self-driving vehicles?

Expected benefits of disruptive technologies such as self-driving vehicles may only materialize if they are publicly accepted. Acceptance increases when implementation is experienced as initial concerns become unfounded and individuals become familiar with the new technology. Fatal accidents, however, negatively affect acceptance. This paper examines citizens' acceptance of self-driving vehicles by contrasting pre- and post-implementation of a self-driving shuttle in Switzerland and before and after the first fatal accident involving a self-driving vehicle in Arizona (USA) in 2018 gaining high media attention. To gauge acceptance, a panel survey using a random sample of 1408 Swiss residents was used. The results indicate that news about the fatal accident abroad exerts a stronger effect on acceptance than experiencing a self-driving bus trial. Latent scepticism causes acceptance to decrease in the event of an accident but only lasts short term. However, acceptance levels are stable and at high levels, which also explains the comparably low familiarity effects. As public acceptance is necessary for technology transitions, this article provides policymakers practical insights on how citizens form preferences towards traffic automation and related policy regulations. More specifically, results show how attitudes change over time while residents experience a trial and the first fatal accident with the technology at hand.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

Safety of autonomous vehicles: what are the insights from experienced industry professionals?

Many drivers support the push towards the automation of transportation. Technology and automotive companies, the general public, and governments are all seen as the main groups pushing the automation revolution forward. The key to this revolution's success will rest with the perception of the professionals in the field as their perception will affect the way that Autonomous Vehicles (AVs) are adopted. This study surveyed 185 experienced industry professionals to gather their comments and opinions on AVs' purported safety benefits and drawbacks. The results in this paper illustrate that the ‘incorrect understanding of the surrounding objects by AVs’ could be the most significant technical concern and may lead to accidents. Some professionals had concerns about trusting the AVs’ safe operation without steering wheels, while some others believed that there is a chance that having steering wheels may increase safety concerns. The findings also suggest that while AVs might reduce the number of accidents, this may not necessarily be the case for the severity of any accidents. The severity may remain unchanged or even increase as a result of the adoption of AVs. This paper's results show that the majority of the professionals consulted agreed that AVs have the potential to increase road safety significantly. However, the consensus was that more research and testing is needed in this field.     

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.     

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 .     

Combined reasoning by automated cooperation

Different reasoning systems have different strengths and weaknesses, and often it is useful to combine these systems to gain as much as possible from their strengths and retain as little as possible from their weaknesses. Of particular interest is the integration of first-order and higher-order techniques. First-order reasoning systems, on the one hand, have reached considerable strength in some niches, but in many areas of mathematics they still cannot reliably solve relatively simple problems, for example, when reasoning about sets, relations, or functions. Higher-order reasoning systems, on the other hand, can solve problems of this kind automatically. But the complexity inherent in their calculi prevents them from solving a whole range of problems. However, while many problems cannot be solved by any one system alone, they can be solved by a combination of these systems.We present a general agent-based methodology for integrating different reasoning systems. It provides a generic integration framework which facilitates the cooperation between diverse reasoners, but can also be refined to enable more efficient, specialist integrations. We empirically evaluate its usefulness, effectiveness and efficiency by case studies involving the integration of first-order and higher-order automated theorem provers, computer algebra systems, and model generators.     

Individually constructed criteria for perception of urban transportation means – An approach based on Kelly’s personal construct theory

Understanding of acceptance of electric mobility has been typically discussed by a comparison of vehicles with different types of propulsion engines, battery electric vehicles and vehicles with an internal combustion engine. Nevertheless, electric mobility comprehends a combination of public transport and electric vehicles. The aim of this paper is to understand peoples’ outlook on electric mobility by identifying shared aspects of the assessment of battery electric vehicles and different user perspectives on transportation. A special research design in the form of repertory grids provides an opportunity to study the underlying causes of the cognitive perceptions and emotions relating to electric mobility. Cognitive interviews motivate respondents to reflect beyond the insights provided by standard forms of interview. Especially for the topic of battery electric vehicles, prejudices - for instance, those propagated by the media - are discarded and the actual requirements and patterns of mobility become visible. The special tasks involved in the interviews lead, for example, to deliberation on how to integrate battery charging processes into existing mobility patterns.This special method reveals that individuals take an interest in more characteristics of modes of transport than those that are usually analysed when researching electric mobility. In addition, three anticipation clusters can be identified for individuals with a higher affinity for cars. First, the perception of battery electric vehicles shows high levels of similarity to cars with internal combustion engines and that differentiating between types of engines is meaningless. Second, battery electric vehicles are perceived as a part of urban public transport. Third, battery electric vehicles are viewed as similar to pedelecs and segways, whereas questions of range, innovation and environmental aspects play a greater role in perceptions. These results lead to the conclusion that when studying the acceptance of BEVs, a comparison between cars with internal combustion engines and battery electric vehicles is not sufficient to grasp the complete user perspective. An analysis within the framework of a wider range of modes of transport is required in order to address people’s transportation needs.     

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.