Volume 43 Issue 2
Apr.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(2): 119-126
SUN Shouzhong, QIN Hua, CHEN Qixuan, RAN Linghua. An Analysis of Factors Influencing the Willingness to Use Automated Driving Function[J]. Journal of Transport Information and Safety, 2025, 43(2): 119-126. doi: 10.3963/j.jssn.1674-4861.2025.02.013
Citation: SUN Shouzhong, QIN Hua, CHEN Qixuan, RAN Linghua. An Analysis of Factors Influencing the Willingness to Use Automated Driving Function[J]. Journal of Transport Information and Safety, 2025, 43(2): 119-126. doi: 10.3963/j.jssn.1674-4861.2025.02.013
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An Analysis of Factors Influencing the Willingness to Use Automated Driving Function

doi: 10.3963/j.jssn.1674-4861.2025.02.013
  • 1.

    School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture. Beijing 102616, China

  • 2.

    China National Institute of Standardization, Beijing 100191, China

  • Received Date: 2024-09-06
    Available Online: 2025-09-29
    Abstract
  • With the accelerated launch of automated driving function (ADF) vehicles on the market, the actual usage rate of its users has shown a low trend. In order to promote the acceptance and application of ADF technology by drivers, it is crucial to analyze the key factors affecting their willingness to use it. Previous studies have examined drivers' willingness to use automation functions in vehicle. However, due to technological limitations at the time, surveyed individuals generally lacked adequate practical experience. In view of this, this study conducts an extensive questionnaire survey for fully experienced user groups. This paper explores the key factors affecting the will-ingness to use from three types of user information: demographics, behavior patterns and feeling evaluation. Based on the literature and established scale, the study devises a questionnaire concerning the willingness to use of Automated Driving Function (ADF). It collects 223 valid questionnaires via online and offline methods. The prediction model of ADF willingness to use is constructed through correlation analysis and hierarchical regression analysis, and the influence of three types of user factors on users' willingness to use is explored. The results show that : ①in the current environment, the constructed predictive model for willingness to use ADF can explain 68.9% of the variance. ②Perceived safety stands out as the predominant forecasting predictor, accounting for 36.2% of the variance in willingness to use. ③New technology orientation, perceived usefulness, trust, understanding and age also have a significant impact on the willingness to use, among which new technology orientation is the biggest factor affecting the willingness to use in behavioral pattern information.④Although the user's behavior pattern has a significant impact on the willingness to use autonomous driving function, it can still improve the willingness to use through a benign driving experience.

     

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    • References(29)
  • [1]
    PARK J, HONG E P, LE H T. Adopting autonomous vehicles: the moderating effects of demographic variables[J]. Journal of Retailing and Consumer Services, 2021, 63(11): 102687.
    [2]
    MARA M, MEYER K. Acceptance of autonomous vehicles: an overview of user-specific, car-specific and contextual determinants[J]. User Experience Design in the Era of Automated Driving, 2022, 980(1): 51-83.
    [3]
    DEGUZMAN C A, DONMEZ B. Knowledge of and trust in advanced driver assistance systems[J]. Accident Analysis & Prevention, 2021, 156(7): 106121.
    [4]
    KAYE S A, SOMORAY K, RODWELL D, et al. Users' acceptance of private automated vehicles: a systematic review and meta-analysis[J]. Journal of Safety Research, 2021, 79(12): 352-367.
    [5]
    LOUW T, MADIGAN R, LEE Y M, et al. Drivers' intentions to use different functionalities of conditionally automated cars: a survey study of 18, 631 drivers from 17 countries[J]. International Journal of Environmental Research and Public Health, 2021, 18(22): 12054. doi: 10.3390/ijerph182212054
    [6]
    NORDHOFF S, LOUW T, INNAMAA S, et al. Using the UTAUT2 model to explain public acceptance of conditionally automated(L3)cars: a questionnaire study among 9, 118 car drivers from eight European countries[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 74 (10): 280-297.
    [7]
    徐亮, 陆洋, 孙造诣, 等. 基于自动驾驶汽车接受度的个性化用户研究[J]. 包装工程, 2023, 44(20): 42-58.

    XU L, LU Y, SUN Z Y, et al. Research on personalized users based on the acceptance of autonomous vehicle[J]. Packaging Engineering, 2023, 44(20): 42-58. (in Chinese)
    [8]
    鄢云珠, 傅忠宁, 岳金田. 车辆怠速起停系统使用意愿结构方程模型及影响因素分析[J]. 交通信息与安全, 2023, 41 (6): 161-170. doi: 10.3963/j.jssn.1674-4861.2023.06.018

    YAN Y Z, FU Z N, YUE J T. An analysis of the influence factors on using intention of vehicle idle start-stop system with a structural equation model[J]. Journal of Transport Information and Safety, 2023, 41(6): 161-170. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.06.018
    [9]
    HEWITT C, POLITIS I, AMANATIDIS T, et al. Assessing public perception of self-driving cars: the autonomous vehicle acceptance model[C]. 24th Intelligent User Interfaces. New York: Association for Computing Machinery, 2019.
    [10]
    FEYS M, ROMBAUT E, VANHAVERBEKE L. Does a test ride influence attitude towards autonomous vehicles? a field experiment with pretest and posttest measurement[J]. Sustainability, 2021, 13(10): 5387. doi: 10.3390/su13105387
    [11]
    WANG S, LI Z, WANG Y, et al. Evidence of automated vehicle safety's influence on people's acceptance of the automated driving technology[J]. Accident Analysis & Prevention, 2024, 195(2): 107381.
    [12]
    SENER I N, ZMUD J, WILLIAMS T. Measures of baseline intent to use automated vehicles: a case study of Texas cities[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 62(4): 66-77.
    [13]
    GREENWOOD P M, BALDWIN C L. Preferred sources of information, knowledge, and acceptance of automated vehicle systems: effects of gender and age[J]. Frontiers in Psychology, 2022, 13(5): 806552.
    [14]
    MA Z, ZHANG Y. Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: effects of automated driving styles and driver's driving styles[J]. Accident Analysis & Prevention, 2021, 159(9): 106238.
    [15]
    YOUSSEF P, PLANT K L, WATERSON B. Narrow passage interactions: a UK-based exploratory survey study to identify factors affecting driver decision-making[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 100(1): 402-418.
    [16]
    XU Z, ZHANG K, MIN H, et al. What drives people to accept automated vehicles? findings from a field experiment[J]. Transportation Research Part C: Emerging Technologies, 2018, 95(11): 320-334.
    [17]
    王云泽, 李英杰, 唐立. 交通运输从业者对自动驾驶接受度建模与分析[J]. 交通运输工程与信息学报, 2023, 21(2): 42-54.

    WANG Y Z, LI Y J, TANG L. Modeling and analysis of acceptance of autonomous driving by transportation practitioners[J]. Journal of Transportation Engineering and Information, 2023, 21(2): 42-54. (in Chinese)
    [18]
    CAO J, LIN L, ZHANG J, et al. The development and validation of the perceived safety of intelligent connected vehicles scale[J]. Accident Analysis & Prevention, 2021, 154(5): 106092.
    [19]
    ZHANG T, TAO D, QU X, et al. The roles of initial trust and perceived risk in public's acceptance of automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2019, 98(1): 207-220.
    [20]
    REAGAN I J, KIDD D G, CICCHINO J B. Driver acceptance of adaptive cruise control and active lane keeping in five production vehicles[C]. 61th Human Factors and Ergonomics Society Annual Meeting, Los Angeles: SAGE Publications, 2017.
    [21]
    NORDHOFF S, STAPLE J, HE X, et al. Perceived safety and trust in SAE level 2 partially automated cars: results from an online questionnaire[J]. Plos one, 2021, 16(12): 260953.
    [22]
    DAVIS F D, BAGOZZI R P, WARSHAW P R. User acceptance of computer technology: a comparison of two theoretical models[J]. Management Science, 1989, 35(8): 982-1003. doi: 10.1287/mnsc.35.8.982
    [23]
    SUN X F. Developing and validating a model for assessing autonomous vehicles acceptance[J]. International Journal of Human-Computer Interaction, 2024, 40(18): 5503-5516. doi: 10.1080/10447318.2023.2238343
    [24]
    HUANG T. Psychological factors affecting potential users'intention to use autonomous vehicles[J]. Plos one, 2023, 18(3): 282915.
    [25]
    LI X, RONG J, LI Z, et al. Modeling drivers' acceptance of augmented reality head-up display in connected environment[J]. Displays, 2022, 75(12): 102307.
    [26]
    李慧慧, 胡启洲, 雷爱国, 等. 基于计划行为理论的驾驶人未礼让行为研究[J]. 交通运输工程与信息学报, 2020, 18 (4): 110-119.

    LI H H, HU Q Z, LEI A G, et al. Driver's noncomity behavior based on planned behavior theory[J]. Journal of Transportation Engineering and Information, 2020, 18(4): 110-119. (in Chinese)
    [27]
    LEE Y M, MADIGAN R, LOUW T, et al. Does users' experience and evaluation of level 3 automated driving functions predict willingness to use: results from an on-road study[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 99(11): 473-484.
    [28]
    KAYE S A, NANDAVAR S, YASMIN S, et al. Consumer knowledge and acceptance of advanced driver assistance systems[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 90(10): 300-311.
    [29]
    QU W, SUN H, GE Y. The effects of trait anxiety and the big five personality traits on self-driving car acceptance[J]. Transportation, 2021, 48(5): 2663-2679. doi: 10.1007/s11116-020-10143-7
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