Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test

被引:5
|
作者
Badweeti K.N. [1 ]
Malaghan V.D. [1 ,2 ]
Pawar D.S. [1 ]
Easa S. [3 ]
机构
[1] Indian Institute of Technology Hyderabad, Department of Civil Engineering, Medak, Kandi
[2] Pandit Deendayal Energy University, Department of Civil Engineering, Gujarat, Gandhinagar
[3] Toronto Metropolitan University, Department of Civil Engineering, Toronto, M5B 2K3, ON
来源
关键词
driving assistance system; forward collision; lane departure warning; road safety; traffic speed recognition;
D O I
10.26599/JICV.2023.9210005
中图分类号
学科分类号
摘要
A large number of reported road collisions are caused by driver inattention, and inappropriate driving behaviour. This study investigated the effectiveness and acceptance of Advanced Driving Assistance Systems (ADAS) for driver age groups, gender, occupation (professional/non-professional), and road type (expressway, urban roads, and semiurban road) based on the Field Operational Test (FOT). The ADAS is provided with assistance features, such as Lane Departure Warning (LDW), Forward Collision Warning (FCW), and Traffic Speed Recognition Warning (TSRW). In total, the FOT involved 30 participants who drove the test vehicle twice (once in the stealth phase and once in the active phase). The FOT included three sections: expressway (20.60 km), urban road (7.2 km), and semi-urban road (13.35 km). A questionnaire was used to determine user acceptance of the ADAS technology. In addition, parametric and non-parametric statistical tests were carried out to determine ADAS's significant effects. The FOT results showed statistically significant differences in the LDW's acceptance and effectiveness for gender, age group, occupation, and road type before and after exposure to ADAS. Male participants showed significant lateral behavior improvement compared to female participants. Old-aged drivers scored the highest acceptance score for the technology compared to middle and young-aged drivers. The subjective ratings ranked the assistance features in descending order as TSRW, LDW, and FCW. This study's findings can support policy development and induce trust in the public for the technology adoption to improve road traffic safety. © 2018 Tsinghua University Press.
引用
收藏
页码:65 / 78
页数:13
相关论文
共 50 条
  • [21] Performance and Environment-Aware Advanced Driving Assistance Systems
    Kasarapu, Sreenitha
    Dinakarrao, Sai Manoj Pudukotai
    IEEE TRANSACTIONS ON COMPUTERS, 2025, 74 (01) : 131 - 142
  • [22] From inside the cabin – truck drivers’ technology acceptance of driving assistance systems
    Gruchmann T.
    Grenzfurtner W.
    Salzmann A.
    Logistics Research, 2024, 17 (01)
  • [23] Operational Design Domain Requirements for Improved Performance of Lane Assistance Systems: A Field Test Study in The Netherlands
    Reddy, Nagarjun
    Farah, Haneen
    Huang, Yilin
    Dekker, Thijs
    Van Arem, Bart
    IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 1 : 237 - 252
  • [24] On the way to autonomous driving: How age influences the acceptance of driver assistance systems
    Guenthner, Timo
    Proff, Heike
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2021, 81 : 586 - 607
  • [25] Miniaturized Advanced Driver Assistance Systems: A Low-Cost Educational Platform for Advanced Driver Assistance Systems and Autonomous Driving
    Gerstmair, Michael
    Gschwandtner, Martin
    Findenig, Rainer
    Lang, Oliver
    Melzer, Alexander
    Huemer, Mario
    IEEE SIGNAL PROCESSING MAGAZINE, 2021, 38 (03) : 105 - 114
  • [26] Perceptions of Risk and Control: Understanding Acceptance of Advanced Driver Assistance Systems
    Joshi, Somya
    Bellet, Thierry
    Bodard, Vanessa
    Amditis, Angelos
    HUMAN-COMPUTER INTERACTION - INTERACT 2009, PT I, 2009, 5726 : 524 - +
  • [27] Driving Into the Memory Wall The Role of Memory for Advanced Driver Assistance Systems and Autonomous Driving
    Jung, Matthias
    McKee, Sally A.
    Sudarshan, Chirag
    Dropmann, Christoph
    Weis, Christian
    Wehn, Norbert
    PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON MEMORY SYSTEMS (MEMSYS 2018), 2018, : 377 - 386
  • [28] Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning
    Dhawan, Kshitij
    Perumal, R. Srinivasa
    Nadesh, R. K.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (17) : 26465 - 26480
  • [29] Evaluation of the effectiveness of advanced driving headlights using a driving simulator
    Suzuki, Keisuke
    Goda, Katsuya
    Doi, Shun'ichi
    Tsukada, Toshihiko
    Higuchi, Kazunori
    Shimaoka, Keiichi
    MECHANICAL ENGINEERING JOURNAL, 2016, 3 (04):
  • [30] Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning
    Kshitij Dhawan
    Srinivasa Perumal R
    Nadesh R. K.
    Multimedia Tools and Applications, 2023, 82 : 26465 - 26480