Abstract： For the acceptance of autonomous vehicles technology, the mapping knowledge domain was applied to analyze and visualize the relationship between the research and development process and the knowledge network in the field of the acceptance of autonomous vehicles technology based on the annual trend of published literature, national cooperation network, publications and periodicals, acceptance behavior theory, influencing factors and data analysis methods. The results show that the United States, Germany and China are the major contributors to the field. The safety of autonomous vehicles technology is the primary affecting factor on public acceptance, and trust, attitude, perceived ease of use, perceived usefulness, perceived risk and social norms are the most important psychological influencing factors on the acceptance. Descriptive statistics and analysis of variance, regression analysis, factor analysis, structural equation model, Logit model and Probit model are the main research methods in the field. However, the existing theoretical models of acceptance of autonomous vehicles technology have low explanatory power, and the symbolic value and key psychological influencing factors on the acceptance of autonomous vehicles technology with Chinese characteristics should be further explored. The samples with deviation and the adding autonomous vehicles technology simulator should be further analyzed.
DI H Y, ZHANG Y H, WANG B, et al. Review on the method and model of intelligent vehicle lateral control[J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(7): 71-81. (in Chinese)
SONG C J, GAO J P, XIE Z X, et al. Typical scene construction and speed prediction of unmanned vehicles[J]. Journal of Chongqing University of Technology (Natural Science), 2022, 36(6): 33-40. (in Chinese)
［3］
PENMETSA P, ADANU E K, WOOD D, et al. Perceptions and expectations of autonomous vehicles ：a snapshot of vulnerable road user opinion[J]. Technological Forecasting and Social Change, 2019, 143: 9-13.
［4］
PETTIGREW S, DANA L M, NORMAN R. Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles[J]. Transport Policy, 2019, 76: 13-20.
［5］
LIU P, XU Z G. Public attitude toward selfdriving vehicles on public roads: direct experience changed ambivalent people to be more positive[J]. Technological Forecasting and Social Change, doi：10.1016/j.techfore.2019.119827.
［6］
NIELSEN T A S, HAUSTEIN S. On sceptics and enthusiasts: what are the expectations towards selfdriving cars?[J]. Transport Policy, 2018, 66: 49-55.
［7］
HOHENBERGER C, SPRRLE M, WELPE I M. Not fearless, but selfenhanced: the effects of anxiety on the willingness to use autonomous cars depend on individual levels of selfenhancement[J]. Technological Forecasting and Social Change, 2017, 116: 40-52.
［8］
HOHENBERGER C, SPRRLE M, WELPE I M. How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 374-385.
TANG L, QIN S D, XU Z G, et al. Research review on public acceptance of autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 131-146. (in Chinese)
CHEN Y, CHEN C M, LIU Z Y, et al. The methodology function of CiteSpace mapping knowledge domains[J]. Studies in Science of Science, 2015, 33(2): 242-253. (in Chinese)
SHUI Y Z, HUANG Z H, GENG J H. Analysis on the latest hotspot and evolution about abroad soccer fitness and conditioning training：based on visualization research of scientific knowledge map[J]. China Sport Science, 2016, 36(1): 67-78. (in Chinese)
ZHANG X L.Disruptive changes and the postlibrary era: toward supplyside structure reform of knowledge services[J]. Journal of Library Science in China, 2018, 44(1): 4-16. (in Chinese)
[13]
HIRSCH J E. An index to quantify an individual′s scientific research output[J]. Proceedings of the National Academy of Sciences, 2005, 102(46): 16569-16572.
[14]
BAMEL U K, PANDEY R, GUPTA A. Safety climate: systematic literature network analysis of 38 years (1980—2018) of research[J]. Accident Analysis & Prevention, doi：10.1016/j.aap.2019.105387.
[15]
MORTENSON M J, VIDGEN R. A computational literature review of the technology acceptance model[J]. International Journal of Information Management, 2016, 36(6): 1248-1259.
[16]
LIU P, YANG R, XU Z G. Public acceptance of fully automated driving: effects of social trust and risk/benefit perceptions: public acceptance of fully automated driving[J]. Risk Analysis: An International Journal, 2019, 39(2): 326-341.
[17]
ZHANG T R, TAO D, QU X D, 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: 207-220.
[18]
VARGO S L, AKAKA M A, WIELAND H. Rethinking the process of diffusion in innovation: a serviceecosystems and institutional perspective[J]. Journal of Business Research, 2020, 116: 526-534.
[19]
RAM S, SHETH J N. Consumer resistance to innovations: the marketing problem and its solutions[J]. Journal of Consumer Marketing, 1989, 6(2): 5-14.
[20]
JING P, XU G, CHEN Y X, et al. The determinants behind the acceptance of autonomous vehicles: a systematic review[J]. Sustainability,doi：10.3390/su12051719.
[21]
HARDMAN S, BERLINER R, TAL G. Who will be the early adopters of automated vehicles? insights from a survey of electric vehicle owners in the United States[J]. Transportation Research Part D: Transport and Environment, 2019, 71: 248-264.
[22]
SHABANPOUR R，GOLSHANI N，SHAMSHIRIPOUR A，et al. Eliciting preferences for adoption of fully automated vehicles using bestworst analysis[J]. Transportation Research Part C：Emerging Technology，2018，93: 463-478.
[23]
CAIRNS S, HARMER C, HOPKIN J, et al. Sociological perspectives on travel and mobilities: a review[J]. Transportation Research Part A: Policy and Practice, 2014, 63: 107-117.
[24]
NORUZOLIAEE M, ZOU B, LIU Y. Roads in transition: integrated modeling of a manufacturertravelerinfrastructure system in a mixed autonomous/human driving environment[J]. Transportation Research Part C: Emerging Technologies, 2018, 90: 307-333.
[25]
PANAGIOTOPOULOS I, DIMITRAKOPOULOS G. An empirical investigation on consumers′ intentions towards autonomous driving[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 773-784.
[26]
LEE J D, SEE K A. Trust in automation: designing for appropriate reliance[J]. Human Factors, 2004，46（1）: 50-80.
[27]
BUCKLEY L, KAYE S A, PRADHAN A K. Psychosocial factors associated with intended use of automated vehicles: a simulated driving study[J]. Accident Analysis & Prevention, 2018, 115: 202-208.
[28]
SOLBRAA BAY A J T. Innovation adoption in robotics: consumer intentions to use autonomous vehicles[D]. Bergen, Norwegian:Norwegian School of Economics，2016.
[29]
VENKATESH V, MORRIS M G, DAVIS G B, et al. User acceptance of information technology: toward a unified view[J]. Management Information Systems Quarterly, 2003, 27(3): 425-478.
[30]
DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. Management Information Systems Quarterly, 1989, 13(3): 318-339.
[31]
RO Y, HA Y. A factor analysis of consumer expectations for autonomous cars[J]. Journal of Computer Information Systems, 2019, 59(1): 52-60.
[32]
LIU P, GUO Q R, REN F, et al. Willingness to pay for selfdriving vehicles: influences of demographic and psychological factors[J]. Transportation Research Part C:Emerging Technologies, 2019, 100: 306-317.
