Using Narrative-Based Video on Gaining Safety Driving: Focusing on Career Resilience and Learning Style in Automated Driving Level 3

2021 ◽  
pp. 787-799
Author(s):  
Maki Arame ◽  
Junko Handa ◽  
Yoshiko Goda ◽  
Masashi Toda ◽  
Ryuichi Matsuba ◽  
...  
Keyword(s):  
Learning Style ◽  
Automated Driving ◽  
Level 3 ◽  
Career Resilience

scholarly journals The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 809 ◽  
Author(s):  
Johannes Hiller ◽  
Sami Koskinen ◽  
Riccardo Berta ◽  
Nisrine Osman ◽  
Ben Nagy ◽  
...  
Keyword(s):  
Data Management ◽  
Reference Architecture ◽  
Automated Driving ◽  
Iterative Processing ◽  
Vehicle Owner ◽  
Level 3 ◽  
System Functioning ◽  
Comprehensive Test ◽  
Vehicle Automation ◽  
Set Up

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

scholarly journals Mode Awareness and Automated Driving—What Is It and How Can It Be Measured?

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 277 ◽  
Author(s):  
Christina Kurpiers ◽  
Bianca Biebl ◽  
Julia Mejia Hernandez ◽  
Florian Raisch
Keyword(s):  
Gold Standard ◽  
Automated Driving ◽  
Active System ◽  
Operational Design ◽  
Automation Systems ◽  
Study Results ◽  
Level 3 ◽  
No Gold Standard ◽  
Level 2 ◽  
Initial Instruction

In SAE (Society of Automotive Engineers) Level 2, the driver has to monitor the traffic situation and system performance at all times, whereas the system assumes responsibility within a certain operational design domain in SAE Level 3. The different responsibility allocation in these automation modes requires the driver to always be aware of the currently active system and its limits to ensure a safe drive. For that reason, current research focuses on identifying factors that might promote mode awareness. There is, however, no gold standard for measuring mode awareness and different approaches are used to assess this highly complex construct. This circumstance complicates the comparability and validity of study results. We thus propose a measurement method that combines the knowledge and the behavior pillar of mode awareness. The latter is represented by the relational attention ratio in manual, Level 2 and Level 3 driving as well as the controllability of a system limit in Level 2. The knowledge aspect of mode awareness is operationalized by a questionnaire on the mental model for the automation systems after an initial instruction as well as an extensive enquiry following the driving sequence. Further assessments of system trust, engagement in non-driving related tasks and subjective mode awareness are proposed.

Attentive User Interfaces to Improve Multitasking and Take-Over Performance in Automated Driving

2019 ◽  
Vol 11 (3) ◽  
pp. 40-58 ◽  
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Andreas Riener
Keyword(s):  
User Interfaces ◽  
Lead Time ◽  
Driving Simulator ◽  
Handheld Devices ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Level 3 ◽  
Traffic Systems ◽  
Consumer Devices ◽  
Positive Effect

Automated vehicles promise engagement in side activities, but demand drivers to resume vehicle control in Take-Over situations. This pattern of alternating tasks thus becomes an issue of sequential multitasking, and it is evident that random interruptions result in a performance drop and are further a source of stress/anxiety. To counteract such drawbacks, this article presents an attention-aware architecture for the integration of consumer devices in level-3/4 vehicles and traffic systems. The proposed solution can increase the lead time for transitions, which is useful to determine suitable timings (e.g., between tasks/subtasks) for interruptions in vehicles. Further, it allows responding to Take-Over-Requests directly on handheld devices in emergencies. Different aspects of the Attentive User Interface (AUI) concept were evaluated in two driving simulator studies. Results, mainly based on Take-Over performance and physiological measurements, confirm the positive effect of AUIs on safety and comfort. Consequently, AUIs should be implemented in future automated vehicles.

scholarly journals KOMUNIKASI MATEMATIKA BERDASARKAN TEORI VAN HIELE PADA MATA KULIAH GEOMETRI DITINJAU DARI GAYA BELAJAR MAHASISWA PROGRAM STUDI PENDIDIKAN MATEMATIKA

2015 ◽  
Vol 9 (2) ◽  
pp. 159
Author(s):  
Fajar Hendro Utomo ◽  
Indah Setyo Wardhani ◽  
Muhammad Abdul Roziq Asrori
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Auditory Learning ◽  
Van Hiele ◽  
Construct Definition ◽  
Kinesthetic Learning ◽  
Van Hiele Theory ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

This objective of this study is to describe competency of mathematic communication based on Van Hiele theory on geometry course viewed from visual and kinesthetic learning styles.  The study was conducted in STKIP PGRI Tulungagung in November 2013 to August 2014, assigning 45 students as sample.  The study revealed that: First,  auditory learning style was achieved by: Level 1 = 0, Level 2 = 2, Level 3 = 9, Level 4 = 4, and Level 5 = 0, averaging at Level 3.  This means that students do not understand when they construct  definition, argument, role, formal deduction they worked;  Second, kinesthetic learning style was achieved by: Level 1 = 0, Level 2 = 6, Level 3 = 10, Level 4 = 2, and Level 5 = 0, averaging at Level 3.  This means that students do not understand on the work as done through auditory learning style.

scholarly journals Kemampuan literasi matematika siswa SMP ditinjau dari gaya belajar

2017 ◽  
Vol 10 (2) ◽  
pp. 222-240
Author(s):  
Muhammad Syawahid ◽  
Susilahudin Putrawangsa
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Visual Learning ◽  
Mathematical Literacy ◽  
Wrong Answer ◽  
Auditory Learning ◽  
Kinesthetic Learning ◽  
Mathematics Literacy ◽  
Level 3 ◽  
Math Literacy

[Bahasa]: Penelitian ini bertujuan untuk mendeskripsikan dan menganalisis kemampuan literasi matematika siswa ditinjau dari gaya belajar. Penelitian ini dilaksanakan di SMPN 1 Mataram kelas VII. Subjek dalam penelitian ini adalah 3 siswa dari 82 siswa kelas VIIIA dan VIIIB yang masing-masing memiliki gaya belajar auditori, visual dan kinestetis. Penelitian ini menggunakan pendekatan kualitatif. Instrumen yang digunakan yaitu angket gaya belajar dan tes kemampuan literasi matematika. Data dianalisis secara deskriptif untuk menggambarkan hasil tes literasi matematika siswa. Hasil penelitian menunjukkan bahwa: 1) Siswa dengan gaya belajar auditori memiliki kemampuan literasi matematika level 4, yang ditunjukkan dengan kemampuan mereka dalam menyelesaikan soal literasi matematika level 4 (soal nomor 1 dan 2) meskipun mereka kesulitan dalam menyelesaikan soal literasi matematika dengan level 3 (soal nomor 3 dan 4). 2) Siswa dengan gaya belajar visual memiliki kemampuan literasi matematika level 3 yang ditunjukkan dengan kemampuan mereka dalam menyelesaikan soal literasi matematika level 3 (soal nomor 3 dan 4) dan tidak mampu menyelesaikan soal literasi matematika level 4 (soal nomor 1 dan 2). 3) Siswa dengan gaya belajar kinestetis memiliki kemampuan literasi matematika level 4 yang ditunjukkan dengan kemampuan mereka dalam menyelesaikan soal literasi matematika level 4 (soal nomor 1) dan level 3 (soal nomor 3 dan 4). Pada soal nomor 2 (level 4) siswa dengan gaya belajar kinestetis kurang teliti sehingga jawaban yang dihasilkan salah.  Kata kunci: Literasi Matematika; Gaya Belajar; Auditori; Visual; Kinestetis [English]: This research aims to describe and analyze student’s mathematic literacy referring to learning style. This research was conducted at SMPN 1 Mataram for VIII class. The subjects are 3 students from 82 students of class VIIIA dan VIIIB who respectively have auditory, visual and kinesthetic learning style. The method used in this research is qualitative. Instruments used in this research are a questionnaire of learning style and tests of mathematical literacy. Data analysis was conducted descriptively to portray students’ mathematics literacy referring to learning styles. This research shows that: 1) The students with auditory learning style are in the 4th level of mathematical literacy, it is indicated by their ability in solving 4th level math literacy problem (question 1 and 2) although they have difficulties in solving 3th level math literacy problem (questions 3 and 4). 2) The students with visual learning styles are in 3rd level of mathematical literacy indicated by their ability to solve 3rd level math literacy problems (questions 3 and 4) and can’t solve the 4th level math literacy problem (questions 1 and 2). 3) The students with kinesthetic learning styles have 4th level of mathematical literacy shown by their ability to solve 4th level of math literacy problems (question 1) and 3rd  level (question 3 and 4). They are less accurate in solving question 2 (4th level) so as they have wrong answer.  Keywords: Mathematics Literacy; Learning Style; Auditory; Visual; Kinesthetic

scholarly journals Does a Confidence Level for Automated Driving Time Estimations Improve the Subjective Evaluation of an Automation HMI?

2020 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Tobias Hecht ◽  
Simon Danner ◽  
Alexander Feierle ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Subjective Evaluation ◽  
Mixed Design ◽  
Automated Driving ◽  
Traffic Conditions ◽  
Driving Time ◽  
Level 3 ◽  
Machine Interface ◽  
The Impact ◽  
The City

Current research in human factors and automated driving is increasingly focusing on predictable transitions instead of urgent and critical take-overs. Predictive human–machine interface (HMI) elements displaying the remaining time until the next request to intervene were identified as a user need, especially when the user is engaging in non-driving related activities (NDRA). However, these estimations are prone to errors due to changing traffic conditions and updated map-based information. Thus, we investigated a confidence display for Level 3 automated driving time estimations. Based on a preliminary study, a confidence display resembling a mobile phone connectivity symbol was developed. In a mixed-design driving simulator study with 32 participants, we assessed the impact of the confidence display concept (within factor) on usability, frustration, trust and acceptance during city and highway automated driving (between factor). During automated driving sections, participants engaged in a naturalistic visual NDRA to create a realistic scenario. Significant effects were found for the scenario: participants in the city experienced higher levels of frustration. However, the confidence display has no significant impact on the subjective evaluation and most participants preferred the baseline HMI without a confidence symbol.

scholarly journals Influence of automated driving on driver’s own localization: a driving simulator study

2018 ◽  
Vol 1 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Ryuichi Umeno ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Operation System ◽  
Content Type ◽  
Automatic Driving ◽  
Driver Drowsiness ◽  
Level 3 ◽  
Fundamental Research ◽  
Operation Group

Purpose Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving. Design/methodology/approach Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system. Findings A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI. Originality/value This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.

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