Assessing error recognition in automated driving

被引:8
|
作者
Spiessl, W. [1 ]
Hussmann, H. [2 ]
机构
[1] BMW Grp, D-80992 Munich, Germany
[2] Univ Munich, Inst Media Informat, D-80333 Munich, Germany
关键词
D O I
10.1049/iet-its.2010.0102
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Technical progress in automated driving research is about to alter the way of driving from manual control towards supervision of automated control. Even if there is still a long way to go, it is worth exploring the ramifications an automated driving task implies. A shift of attention towards secondary activities in the car is acceptable as long as the automation is guaranteed not to fail. An interesting question appears when automation fails only in rare cases. In this study, the authors are exploring drivers' ability of error recognition in an automated driving scenario with a focus on simultaneously performing secondary tasks. The authors use a newly developed method for this, the autonomous lane change test. The authors find clear effects of secondary tasks on automation supervision, with tasks requiring strong engagement to be most noticeable.
引用
收藏
页码:103 / 111
页数:9
相关论文
共 50 条
  • [1] Assessing the Safety of Environment Perception in Automated Driving Vehicles
    Berk, Mario
    Schubert, Olaf
    Kroll, Hans-Martin
    Buschardt, Boris
    Straub, Daniel
    SAE INTERNATIONAL JOURNAL OF TRANSPORTATION SAFETY, 2020, 8 (01) : 49 - 74
  • [2] Frameworks for assessing societal impacts of automated driving technology
    Almlof, Erik
    Zhao, Xiaoyun
    Pernestal, Anna
    Jenelius, Erik
    Nybacka, Mikael
    TRANSPORTATION PLANNING AND TECHNOLOGY, 2022, 45 (07) : 545 - 572
  • [3] Deep Convolutional Traffic Light Recognition for Automated Driving
    Bach, Martin
    Stumper, Daniel
    Dietmayer, Klaus
    2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2018, : 851 - 858
  • [4] Automated driving recognition technologies for adverse weather conditions
    Yoneda, Keisuke
    Suganuma, Naoki
    Yanase, Ryo
    Aldibaja, Mohammad
    IATSS RESEARCH, 2019, 43 (04) : 253 - 262
  • [5] Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving
    Westhofen, Lukas
    Neurohr, Christian
    Butz, Martin
    Scholtes, Maike
    Schuldes, Michael
    IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 3 : 519 - 538
  • [6] Recognition Performance Evaluation of Sensors Used in Automated Driving
    Journal of the Institute of Electrical Engineers of Japan, 2023, 143 (08): : 521 - 524
  • [7] Assessing the travel impacts of subnetworks for automated driving: An exploratory study
    Madadi, Bahman
    van Nes, Rob
    Snelder, Maaike
    van Arem, Bart
    CASE STUDIES ON TRANSPORT POLICY, 2019, 7 (01) : 48 - 56
  • [8] Recognition System of Hand Signals of a Police Officer for Automated Driving
    Ono, Shintaro
    Kida, Atsumu
    Suda, Yoshihiro
    Watanabe, Takanoshin
    Karg, Michelle
    2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,
  • [9] Understanding Take-Over in Automated Driving: A Human Error Analysis
    Li, Jue
    Liu, Long
    Gu, Liwen
    HCI IN MOBILITY, TRANSPORT, AND AUTOMOTIVE SYSTEMS, MOBITAS 2021, 2021, 12791 : 281 - 295
  • [10] Personal driving diary: Automated recognition of driving events from first-person videos
    Ryoo, M. S.
    Choi, Sunglok
    Joung, Ji Hoon
    Lee, Jae-Yeong
    Yu, Wonpil
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2013, 117 (10) : 1299 - 1312