Understanding the influence of in-vehicle information systems on range stress – Insights from an electric vehicle field experiment

Battery electric vehicle (BEV) drivers frequently report concerns of becoming stranded as a result of battery depletion, widely referred to as range stress, which constitutes a main obstacle to the adoption of these vehicles. One promising solution identified to mitigate range stress is to provide appropriate and accurate information through in-vehicle information systems (IVISs). Therefore, we aim to gain a broader understanding on the influence of specific IVISs on drivers’ range stress perception. To accomplish this, we performed a BEV field experiment in real traffic situations. By placing participants in a critical range situation, we could measure their psychometric range appraisal and psychophysiological feedback. The results of the psychometric evaluation revealed that individuals perceived the critical range situation as less challenging and threatening with the provided IVISs. Physiological indicators were found to be useful in complementing questionnaire-based evaluation methods within the field of range stress. Although the findings of the psychophysiological measurement revealed that IVISs-equipped participants exhibited increases in stress perception over time due to volatile range feedback, the average stress level was observed to be lower throughout the entire driving task.     

Faster Teaching via POMDP Planning

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision‐theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept‐learning tasks, we show that this technique can accelerate learning relative to baseline performance.     

Defining answer classes using resolution refutation

Resolution theorem proving provides a useful paradigm for the exploration of question answering. A partition of the clauses generated during resolution refutation based on their syntactic structure is presented. The three classes comprising this partition correspond to semantically intuitive types of answers. This work encompasses and expands upon previous work on question answering in a theorem proving paradigm, which began with the association of answers with proofs. A complete, formal definition of what is meant by answer in the context of resolution theorem proving is presented. In this context, clauses that are relevant are all identified as answers, where relevance is determined with respect to a question and knowledge base: any clause descended from the clause form of a negated question is deemed relevant. This definition of relevance is not in and of itself novel; rather, it is the way in which the set of relevant clauses is partitioned that provides the key to interpreting clauses as answers. The three answer classes identified are: specific, generic, and hypothetical. These classes are formally distinguished by the way in which literals in a clause share variables, with class membership based on a property termed the closure of variable sharing of a literal. The results presented provide a foundation for further work by establishing a context-independent logical pragmatics of question answering.     

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

In the near future, automated vehicles (AVs) will enter the urban transport system. This fact will lead to mixed traffic consisting of AVs, human car drivers and vulnerable road users. Since the AV’s passenger no longer has to monitor the driving scene, conventional communication does not exist anymore, which is essential for traffic efficiency and safety. In research, there are plenty of studies focusing on how AVs could communicate with pedestrians. One approach is to use external human-machine interfaces (eHMIs) on the AV’s surface. In contrast to the studies dealing with AV-pedestrian communication, this paper focuses on communication strategies of AVs with drivers of regular vehicles in different road bottleneck scenarios. The eHMI development and design is building on previously defined requirements and on fundamentals of human visual perception. After designing several eHMI drafts, we conducted a user survey with 29 participants resulting in the final eHMI concept. The evaluation of the evolved eHMI was conducted in a driving simulator experiment with 43 participants investigating the AV-human driver interaction at road bottlenecks. The participants were assigned either to the experimental group being faced with the eHMI or to the baseline group without explicit communication. The results show significantly shorter passing times and fewer crashes among the human drivers in the group with the eHMI. Additionally, the paper researches the aftereffects of an automation failure, where the AV first yields the right of way and then changes its strategy and insisted on priority. Experiencing the automation failure is reflected in increased passing times, reduced acceptance ratings and a lower perceived usefulness. In conclusion, especially in unregulated bottleneck scenarios flawless communication via eHMIs increases traffic efficiency and safety.     

Take over! A video-clip study measuring attention,situation awareness,and decision-making in the face of an impending hazard

In highly automated driving, drivers occasionally need to take over control of the car due to limitations of the automated driving system. Research has shown that visually distracted drivers need about 7 s to regain situation awareness (SA). However, it is unknown whether the presence of a hazard affects SA. In the present experiment, 32 participants watched animated video clips from a driver’s perspective while their eyes were recorded using eye-tracking equipment. The videos had lengths between 1 and 20 s and contained either no hazard or an impending crash in the form of a stationary car in the ego lane. After each video, participants had to (1) decide (no need to take over, evade left, evade right, brake only), (2) rate the danger of the situation, (3) rebuild the situation from a top-down perspective, and (4) rate the difficulty of the rebuilding task. The results showed that the hazard situations were experienced as more dangerous than the non-hazard situations, as inferred from self-reported danger and pupil diameter. However, there were no major differences in SA: hazard and non-hazard situations yielded equivalent speed and distance errors in the rebuilding task and equivalent self-reported difficulty scores. An exception occurred for the shortest time budget (1 s) videos, where participants showed impaired SA in the hazard condition, presumably because the threat inhibited participants from looking into the rear-view mirror. Correlations between measures of SA and decision-making accuracy were low to moderate. It is concluded that hazards do not substantially affect the global awareness of the traffic situation, except for short time budgets.     

How religion and morality correlate in age of society 5.0: Statistical analysis of emotional and moral associations with Buddhist religious terms appearing on Japanese blogs

In this paper we analyzed how much religious vocabulary, in particular Buddhist vocabulary taken from the largest online dictionary of Buddhist terms, is present in everyday social space of Japanese people, particularly, in Japanese blog entries appearing on a popular blog service (Ameba blogs). We interpreted the level of everyday usage of Buddhist terms as appearance of such terms in the consciousness of people. We further analyzed what emotional and moral associations such contents generate. In particular, we analyzed whether expressions containing Buddhist vocabulary are considered appropriate or not from a moral point of view, as well as the emotional response of Internet users to Buddhist terminology. As a result of analyzing the data, we found out that Buddhist terms were in fact not absent as a theme from Japanese blogs and generated a strong emotional response. However, while the general reaction to several expressions using Buddhist terms was as expected, there were sometimes surprising twists in terms of social consequences, major discrepancies between what is perceived as ethically correct behavior between the Buddhist doctrine and the reasoning of the general population, as well as a considerate number of terms which have lost their original meaning and instead became slang expressions.     

Theorema: Towards computer-aided mathematical theory exploration

Theorema is a project that aims at supporting the entire process of mathematical theory exploration within one coherent logic and software system. This survey paper illustrates the style of Theorema-supported mathematical theory exploration by a case study (the automated synthesis of an algorithm for the construction of Gröbner Bases) and gives an overview on some reasoners and organizational tools for theory exploration developed in the Theorema project.     

On-Road vehicle study of the experience of automated driving

Prior studies of automated driving have focused on drivers’ evaluations of advanced driving assistance systems and their knowledge of the technology. An on-road experiment with novice drivers who had never used automated systems was conducted to examine the effects of the automation on the driving experience. Participants drove a Tesla Model 3 sedan with level 2 automation engaged or not engaged on a 4-lane interstate freeway. They reported that driving was more enjoyable and less stressful during automated driving than manual driving. They also indicated that they were less anxious and nervous, and able to relax more with the automation. Their intentions to use and purchase automated systems in the future were correlated with the favorableness of their automated driving experiences. The positive experiences of the first-time users suggest that consumers may not need a great deal of persuading to develop an appreciation for partially automated vehicles.     

It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts

Automated driving (AD), which takes full responsibility for the driving task in certain conditions, is currently being developed. An important concern in AD is how to design a take-over request (TOR) that mitigates automation effects (e.g., delayed responses to conflict scenarios) that previous literature from simulator experiments has shown can occur. To address this concern, this study aims to investigate and compare driver responses to TORs and a lead-vehicle cut-out scenario under three conditions: (1) after a period of AD with a TOR issued early (18 s time-to-collision), (2) same as (1) except with a TOR issued late (9 s time-to-collision), and (3) baseline, with adaptive cruise control (ACC). This paper also compares the results to those of a previous study using the same conflict scenario but with near-perfect assisted driving system (SAE Level 2). The lead-vehicle cut-out scenario was encountered on a test track after 30 minutes driving with either ACC or AD. In AD the TOR was issued prior to the conflict object was revealed to the participants when the lead vehicle performed the cut-out (at conflict onset). This TOR strategy differed from previous driving-simulator studies that issued the TOR at conflict onset. The participants had to respond by steering and/or braking to avoid a crash. Our findings show that, independent of TOR timing, the drivers required similar amounts of time to 1) direct their first glance to the human–machine interface, 2) look forward, 3) end their secondary task, 4) put their hands on the steering wheel, and 5) deactivate automation. However, when the TOR was issued early rather than late, they started to brake earlier (even before conflict onset). All participants successfully managed to avoid crashing with the object, independent of the condition. AD with an early TOR resulted in the earliest response, while ACC drivers responded slightly earlier than the drivers in AD with the late TOR. Our findings do not support the findings of severe automation effects in previous driving-simulator studies. One reason for the difference is that when a TOR is issued prior to conflict onset, drivers are given the time needed for their preparatory actions (e.g., placing hands on the wheel, deactivating AD) that is not needed when driving with ACC or in manual driving (baseline), before having to respond to the conflict scenario. Thus, at conflict onset the drivers in AD are as ready to act (hands on wheel, eyes forward) as the drivers in the baseline and can perform an avoidance manoeuvre similar as to the baseline drive. Overall, the present study shows that AD does not need to end up in a highly critical situation if the TOR is issued early enough. In fact, AD with an early TOR may be safer than driving with ACC, because in the former drivers are more likely to brake earlier in preparation for the conflict. Finally, a TOR clearly communicates the need for drivers to resume manual control and handle potential events when AD has been deactivated. In our study, once the drivers had taken control, they clearly understood their responsibilities to respond to the conflict, in contrast to a previous study with a similar, near-perfect assisted driving system.     

Humanizing autonomous vehicle driving: Understanding,modeling and impact assessment

The advent of autonomous vehicles brings major changes in the transportation systems influencing the infrastructure design, the network performance, as well as driving functions and habits. The penetration rate of this new technology highly depends on the acceptance of the automated driving services and functions, as well as on their impacts on various traffic, user oriented and environmental aspects. This research aims to present a methodological framework aiming to facilitate the modelling of the behaviour of new AV driving systems and their impacts on traffic, safety and environment. This framework introduces a stepwise approach, which will be leveraged by stakeholders in order to evaluate the new technology and its components at the design or implementation phase in order to increase acceptance and favor the adoption of the new technology. The proposed framework consists of four sequential steps: i. conceptual design, ii. data collection, processing and mining, iii. modelling and iv. autonomous vehicles impact assessment. The connection between these steps is illustrated and various Key Performance Indicators are specified for each impact area. The paper ends with highlighting some conceptual and modeling challenges that may critically affect the study of acceptance of autonomous vehicles in future mobility scenarios.