Human-Machine Interface Evaluation Using EEG in Driving Simulator

被引:1
|
作者
Liu, Yuan-Cheng [1 ]
Figalova, Nikol [2 ]
Baumann, Martin [3 ]
Bengler, Klaus [1 ]
机构
[1] Tech Univ Munich, Chair Ergon, Munich, Germany
[2] Ulm Univ, Dept Clin & Hlth Psychol, Ulm, Germany
[3] Ulm Univ, Dept Human Factors, Ulm, Germany
关键词
D O I
10.1109/IV55152.2023.10186567
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG's spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Driving across Markets: An Analysis of a Human-Machine Interface in Different International Contexts
    Sogemeier, Denise
    Forster, Yannick
    Naujoks, Frederik
    Krems, Josef F.
    Keinath, Andreas
    INFORMATION, 2024, 15 (06)
  • [32] Test procedure for evaluating the human-machine interface of vehicles with automated driving systems
    Naujoks, Frederik
    Hergeth, Sebastian
    Wiedemann, Katharina
    Schoemig, Nadja
    Forster, Yannick
    Keinath, Andreas
    TRAFFIC INJURY PREVENTION, 2019, 20 : S146 - S151
  • [33] Design and Evaluation of Human-Machine Interface of Simulation Training System
    Li, Meng
    PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON MAN-MACHINE-ENVIRONMENT SYSTEM ENGINEERING, 2015, 356 : 475 - 481
  • [34] Exploration of Explainable AI in Context of Human-Machine Interface for the Assistive Driving System
    Chaczko, Zenon
    Kulbacki, Marek
    Gudzbeler, Grzegorz
    Alsawwaf, Mohammad
    Thai-Chyzhykau, Ilya
    Wajs-Chaczko, Peter
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2020), PT II, 2020, 12034 : 507 - 516
  • [35] Human-machine interface for wheelchair control with EMG and its evaluation
    Han, JS
    Bien, ZZ
    Kim, DJ
    Lee, HE
    Kim, JS
    PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH, 2003, 25 : 1602 - 1605
  • [36] Evaluation of contactless human-machine interface for robotic surgical training
    Despinoy, Fabien
    Zemiti, Nabil
    Forestier, Germain
    Sanchez, Alonso
    Jannin, Pierre
    Poignet, Philippe
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2018, 13 (01) : 13 - 24
  • [37] Evaluation of different interface designs for human-machine interaction in vehicles
    Cegovnik, Tomaz
    Stojmenova, Kristina
    Tartalja, Igor
    Sodnik, Jaka
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (29-30) : 21361 - 21388
  • [38] UUX Evaluation of a Digitally Advanced Human-Machine Interface for Excavators
    Lorenz, Sebastian
    Helmert, Jens R.
    Anders, Ruben
    Woelfe, Christian
    Krzywinski, Jens
    MULTIMODAL TECHNOLOGIES AND INTERACTION, 2020, 4 (03) : 1 - 20
  • [39] Evaluation of different interface designs for human-machine interaction in vehicles
    Tomaž Čegovnik
    Kristina Stojmenova
    Igor Tartalja
    Jaka Sodnik
    Multimedia Tools and Applications, 2020, 79 : 21361 - 21388
  • [40] A Human-Machine Interface Evaluation Method Based on Balancing Principles
    Ha, Jun Su
    24TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION, 2013, 2014, 69 : 13 - 19