Command-Based Driving for Tactical Control of Highly Automated Vehicles

被引:4
|
作者
Habibovic, Azra [1 ]
Andersson, Jonas [1 ]
Nilsson, Jan [2 ]
Nilsson, Maria [1 ]
Edgren, Claes [3 ]
机构
[1] Viktoria Swedish ICT, Lindholmspiren 3A, S-41756 Gothenburg, Sweden
[2] Semcon Sweden AB, S-41780 Gothenburg, Sweden
[3] Volvo Car Corp, S-40531 Gothenburg, Sweden
关键词
Tactical control; Command-based driving; Highly automated vehicle; Feeling of control; Satisfaction; User experience; Wizard of Oz;
D O I
10.1007/978-3-319-41682-3_42
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As vehicles become highly automated, their drivers become more passive. A concern is it may take drivers out of the control loop, causing reduced satisfaction and perceived control. The study explores whether or not drivers feel the need to control tactical decisions when operating highly automated vehicles. An experiment involving 17 drivers was carried out in a driving simulator. Each driver tested two different tactical controllers, allowing him/her to give various tactical commands to the vehicle (e.g., overtake, park). The results indicate that the drivers experienced a need to affect tactical decisions of highly automated vehicles. Several of the tactical commands were found useful, especially on rural roads and highways. It also gave them a feeling of being in control of the vehicle, suggesting that command-based driving might be a way to keep drivers in the control loop.
引用
收藏
页码:499 / 510
页数:12
相关论文
共 50 条
  • [31] TACTICAL COMMAND AND CONTROL GRAPHICAL DISPLAY REQUIREMENTS
    USECHAK, D
    [J]. NCGA 89 CONFERENCE PROCEEDINGS, VOLS 1-3, 1989, : A46 - A51
  • [32] INFORMATION SYSTEM ORGANIZATION FOR TACTICAL COMMAND AND CONTROL
    CRAIG, LJ
    [J]. IEEE TRANSACTIONS ON MILITARY ELECTRONICS, 1965, MIL9 (02): : 88 - &
  • [33] Comfort and adaptive cruise control in highly automated vehicles
    Siebert, Felix
    Oehl, Michael
    Mahlfeld, Wiebke
    Pfister, Hans-Ruediger
    Hoeger, Rainer
    [J]. INTERNATIONAL JOURNAL OF PSYCHOLOGY, 2012, 47 : 768 - 768
  • [34] Risk Assessment of Highly Automated Vehicles with Naturalistic Driving Data: A Surrogate-based Optimization Method
    Zhang, He
    Zhou, Huajun
    Sun, Jian
    Tian, Ye
    [J]. 2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2022, : 580 - 585
  • [35] Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios
    Wang, Siyang
    Lin, Xianke
    [J]. Applied Energy, 2020, 271
  • [36] Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios
    Wang, Siyang
    Lin, Xianke
    [J]. APPLIED ENERGY, 2020, 271
  • [37] Highly Automated Vehicles
    Andreas Fuchs
    [J]. ATZoffhighway worldwide, 2016, 9 (4): : 3 - 3
  • [38] Switching command-based whole-body operation method for humanoid robots
    Neo, ES
    Yokoi, K
    Kajita, S
    Kanehiro, F
    Tanie, K
    [J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2005, 10 (05) : 546 - 559
  • [39] The Research of Multilevel Takeover Alert Information Design for Highly Automated Driving Vehicles
    Jiang, Lijun
    Cao, Simin
    Li, Zhelin
    Zhang, Yu
    Zhang, Zequan
    [J]. MAN-MACHINE-ENVIRONMENT SYSTEM ENGINEERING (MMESE 2019), 2020, 576 : 377 - 384
  • [40] Comfortable driving control for connected automated vehicles based on deep reinforcement learning and knowledge transfer
    Wu, Chuna
    Chen, Jing
    Yao, Jinqiang
    Chen, Tianyi
    Cao, Jing
    Zhao, Cong
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2024,