IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v175y2023ics0965856423001775.html

How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference

Author

Listed:
  • Cai, Yunhao
  • Jing, Peng
  • Wang, Baihui
  • Jiang, Chengxi
  • Wang, Yuan

Abstract

Traffic accidents caused by drivers’ over-reliance on SAE L2 advanced driver assistance systems (ADAS) have become a new type of accident worthy of attention. There is growing concern that the “over-hype” about automation technology produced by manufacturers or the media might affect the formation of public misconceptions about autonomous vehicles (AVs), further influencing their unsafe interaction behaviors with the system. The purpose of our study was to explore whether and how “over-hype” in various contexts affects public misconceptions about AVs from the view of causality. We conducted a randomized controlled trial with a control group of 396 participants and a treatment group of 545 participants drawn from Zhenjiang, China. We applied Double Machine Learning (DML), ordered logit models, and significant difference tests to estimate the impact of “over-hype” in various contexts. The results found that the statement that “over-hype” caused public misconceptions was less rigorous. Significant causality varies among contexts of “over-hype” and aspects of misconceptions. Specifically, “over-hype” in fatal accident reports will lead to availability misconception about AVs, but not safety misconception, suggesting that participants believed AVs were available nowadays but not safe as human drivers. “Over-hype” that exaggerates ADAS capabilities has the greatest average treatment effect on public misconceptions, especially the safety misconception. We found that the young and the more educated are more likely to have safety and availability misconceptions under the “over-hype” of some contexts. However, the effects of “over-hype” varied less among groups with different socio-economic factors on the whole, which revealed the universality of the influence of “over-hype.” The results provided empirical evidence for regulating propaganda about AV technology and provided practical insights on how to introduce AV technology to the general public properly.

Suggested Citation

  • Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:transa:v:175:y:2023:i:c:s0965856423001775
    DOI: 10.1016/j.tra.2023.103757
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856423001775
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2023.103757?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kenneth F Schulz & Douglas G Altman & David Moher & for the CONSORT Group, 2010. "CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials," PLOS Medicine, Public Library of Science, vol. 7(3), pages 1-7, March.
    2. Limin Tan & Changxi Ma & Xuecai Xu & Jin Xu, 2019. "Choice Behavior of Autonomous Vehicles Based on Logistic Models," Sustainability, MDPI, vol. 12(1), pages 1-16, December.
    3. Charness, Gary & Gneezy, Uri & Kuhn, Michael A., 2012. "Experimental methods: Between-subject and within-subject design," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 1-8.
    4. Keele, Luke, 2015. "The Statistics of Causal Inference: A View from Political Methodology," Political Analysis, Cambridge University Press, vol. 23(3), pages 313-335, July.
    5. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    6. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    7. Lane, Bradley W., 2019. "Revisiting ‘An unpopular essay on transportation:’ The outcomes of old myths and the implications of new technologies for the sustainability of transport," Journal of Transport Geography, Elsevier, vol. 81(C).
    8. van Lente, Harro & Spitters, Charlotte & Peine, Alexander, 2013. "Comparing technological hype cycles: Towards a theory," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1615-1628.
    9. Edmond Awad & Sydney Levine & Max Kleiman-Weiner & Sohan Dsouza & Joshua B. Tenenbaum & Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2020. "Drivers are blamed more than their automated cars when both make mistakes," Nature Human Behaviour, Nature, vol. 4(2), pages 134-143, February.
    10. Luo, Rachel & Fan, Yichun & Yang, Xin & Zhao, Jinhua & Zheng, Siqi, 2021. "The impact of social externality information on fostering sustainable travel mode choice: A behavioral experiment in Zhengzhou, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 127-145.
    11. Yu, Yuewen & Luo, Xia & Su, Qiming & Peng, Weikang, 2023. "A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Du, Manqing & Zhang, Tingru & Liu, Jinting & Xu, Zhigang & Liu, Peng, 2022. "Rumors in the air? Exploring public misconceptions about automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 237-252.
    13. Lin, Shichao & Zhu, Songwei & Li, Xiangmin & Li, Ruimin, 2022. "Effects of strict vehicle restrictions on various travel modes: A case study of Zhengzhou, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 310-323.
    14. Rashidi, Taha Hossein & Waller, Travis & Axhausen, Kay, 2020. "Reduced value of time for autonomous vehicle users: Myth or reality?," Transport Policy, Elsevier, vol. 95(C), pages 30-36.
    15. Barry Dewitt & Baruch Fischhoff & Nils-Eric Sahlin, 2019. "‘Moral machine’ experiment is no basis for policymaking," Nature, Nature, vol. 567(7746), pages 31-31, March.
    16. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    17. Alexandros Nikitas & Eric Tchouamou Njoya & Samir Dani, 2019. "Examining the myths of connected and autonomous vehicles: analysing the pathway to a driverless mobility paradigm," International Journal of Automotive Technology and Management, Inderscience Enterprises Ltd, vol. 19(1/2), pages 10-30.
    18. Hudson, John & Orviska, Marta & Hunady, Jan, 2019. "People’s attitudes to autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 164-176.
    19. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Xiang & Sun, Haojie & Pei, Xiaoyang & Guan, Linghui & Wang, Zihao, 2024. "Evolution of technology investment and development of robotaxi services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    2. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    2. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    3. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    4. Linlin Wu & Zhen Qin & Peng Jing & Ying Xue & Danning Shao & Pan Luo, 2025. "The Causal Effect of Active School Travel on Children’s Subjective Well-Being: Evidence from the China Family Panel Survey," Journal of Happiness Studies, Springer, vol. 26(6), pages 1-28, August.
    5. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    6. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
    7. Lili Liu & Tiantian Feng & Cheng Zhong & Mingli Cui & Haoran Wang, 2025. "Synergy pathway innovation of carbon emission trading, tradable green certificate and green power trading policies: achieving China’s dual carbon goals," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-28, December.
    8. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    9. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    10. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    11. Lei Suo & Linsen Zhu & Haiying Feng & Wei Li, 2025. "The Impact of Urbanization Level on Urban Ecological Resilience and Its Role Mechanisms: A Case Study of Resource-Based Cities in China," Sustainability, MDPI, vol. 17(19), pages 1-28, September.
    12. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    13. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    14. Yantuan Yu & Ning Zhang, 2025. "Revealing the Power of Market-Based Energy Policy: Evidence from China’s Energy Quota Trading System Using Machine Learning," Working papers 2025rwp-258, Yonsei University, Yonsei Economics Research Institute.
    15. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    16. Heiler, Phillip, 2024. "Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political polarization," Journal of Econometrics, Elsevier, vol. 244(1).
    17. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    18. Guiming Liao & Zheli Hu & Zhichen Yang & Qianlin Yin & Jiahui Chen, 2025. "The Perception of Policy Uncertainty and the Labor Income Share of Firms: Empirical Research Based on Double Machine Learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(2), pages 999-1011, March.
    19. Nora Bearth & Michael Lechner & Jana Mareckova & Fabian Muny, 2025. "Fairness-Aware and Interpretable Policy Learning," Papers 2509.12119, arXiv.org.
    20. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:175:y:2023:i:c:s0965856423001775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.