The psychology of driving automation: A discussion with Professor Don Norman

Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

Prediction of drivers’ performance in highly automated vehicles

Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

Teaching for Deep Learning in a Second Grade Literacy Classroom

From a New Literacies Studies (NLS) perspective, deep learning involves the acquisition of social and cultural competencies valued within a disciplinary community, not merely propositional displays of what one knows. Drawn from a year-long qualitative inquiry, this case study examines how one exemplary second-grade literacy teacher taught toward deep learning, using a pedagogy of multiliteracies (New London Group, 1996). Selected episodes of instruction were analyzed in two phases. Initially, data were examined for evidence of three main competency sets of deep learning--cognitive, inter-personal, and intra-personal (National Research Council, 2012). In the latter phase, analysis focused on the teacher’s pedagogical stances of situated practice, overt instruction, critical framing, and transformed practice (NLG, 1996). Findings suggest that teaching for deep learning involved overt instruction of cognitive processes. Additionally, the teacher modeled critical framing processes of disciplinary practices situated within student-centered projects. Implications include how responsive literacy instruction may prime students’ readiness to cultivate deep learning competencies. Inside today’s classrooms, teaching for deep learning may necessitate addressing domain-based practices together with socially oriented work dispositions, allowing for both a production-oriented, text-centric view of learning (NLG, 1996) and an orientation toward space, spontaneity, and emergence in literacy engagement (Leander & Boldt, 2013)

Teaching for Deep Learning in a Second Grade Literacy Classroom

From a New Literacies Studies (NLS) perspective, deep learning involves the acquisition of social and cultural competencies valued within a disciplinary community, not merely propositional displays of what one knows. Drawn from a year-long qualitative inquiry, this case study examines how one exemplary second-grade literacy teacher taught toward deep learning, using a pedagogy of multiliteracies (New London Group, 1996). Selected episodes of instruction were analyzed in two phases. Initially, data were examined for evidence of three main competency sets of deep learning--cognitive, inter-personal, and intra-personal (National Research Council, 2012). In the latter phase, analysis focused on the teacher’s pedagogical stances of situated practice, overt instruction, critical framing, and transformed practice (NLG, 1996). Findings suggest that teaching for deep learning involved overt instruction of cognitive processes. Additionally, the teacher modeled critical framing processes of disciplinary practices situated within student-centered projects. Implications include how responsive literacy instruction may prime students’ readiness to cultivate deep learning competencies. Inside today’s classrooms, teaching for deep learning may necessitate addressing domain-based practices together with socially oriented work dispositions, allowing for both a production-oriented, text-centric view of learning (NLG, 1996) and an orientation toward space, spontaneity, and emergence in literacy engagement (Leander & Boldt, 2013)

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Evaluation of critical success factors through the use of mind map in the logistic process of a customs clearance commissioner in a brazilian company

The objective of this work is to present, by the means of Mind Map, the critical success factors of the project developed by a Customs Clearance Commissioner. The research methodology used is exploratory, through interviews, based on the Delphi technique to obtain consensus, combined with the exploratory-descriptive analysis of a case study. The case study was observed in an unsystematic way, with an unstructured questionnaire. The conclusions presented here reveal two critical success factors in the import of parts and pieces, that are compounds of machines and industrial equipment, showing that the Mind Map is an innovative application for managing logistics projects, enabling better visualization of critical points, which brings improvements in the planning of activities for areas of knowledge, applied to the deployment project developed by the Customs Clearance Commissioner

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 224 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1984

Case studies of cycles in developing a physics lesson.

Children\u27s reasoning and learning about levers and simple machines were investigated in this study. The study included several cycles of design, test and clinical interview tutoring sessions and the two final cycles are presented here. The methodology combined the use of qualitative clinical interviewing data and quantitative summative data: quantitative evaluations provided an overview of the lessons\u27 effects, while qualitative, formative lesson evaluations allowed deeper insights into learning and reasoning processes. Three groups of participants were interviewed about the pretest, lesson and posttest. The pre- and posttests were standardized, and several new and widespread misconceptions about levers have been discovered that are less accurate or general than conventional conceptions. In experiment 1 the pre-posttest comparison between the control group and experimental group 1 showed that there were no differences and the instruction in experiment 2 was revised considerably as a result of the formative evaluation findings. Significant improvements were apparent for experimental group 2 with regard to conceptual change and for transfer when compared with experimental group 1--evident in group 2 students\u27 ability to transfer their acquired knowledge to complex and compound levers and in conceptual changes apparent in simple levers questions. Lesson 1 was essentially a bridging lesson where intuitive anchoring examples were extended analogically via intermediate bridging cases to a target situation. The findings from lesson 1 suggested that reasoning from extreme case situations of levers might be instructionally useful, and this hypothesis was confirmed by results from experiment 2, where the instructional sequences based on extreme case reasoning proved to be powerful facilitators of the construction of mechanistic models by the students that fostered conceptual change and learning. The following directions for further research are suggested: students\u27 conceptual models have implications for teaching and learning that are poorly understood at this stage, and research on instruction that employs experts\u27 non-formal reasoning strategies should be encouraged