Page 349 - 《软件学报》2026年第3期
P. 349

1312                                                       软件学报  2026  年第  37  卷第  3  期


                      transition of automated vehicle. In: Proc. of the 2019 Int’l Conf. on Information and Communication Technology Convergence (ICTC).
                      Jeju: IEEE, 2019. 1394–1396. [doi: 10.1109/ICTC46691.2019.8939867]
                 [85]   Yao H, An SY, Zhou HP, Itoh M. How does driver takeover worsen in a sudden system failure of conditionally automated driving? In:
                      Proc.  of  the  59th  Annual  Conf.  of  the  Society  of  Instrument  and  Control  Engineers  of  Japan  (SICE).  Chiang  Mai:  IEEE,  2020.
                      1469–1474. [doi: 10.23919/SICE48898.2020.9240345]
                 [86]   Yamabe S, Kawaguchi S, Anakubo M. Comfortable awakening method for sleeping driver during autonomous driving. Int’l Journal of
                      Intelligent Transportation Systems Research, 2022, 20(1): 266–278. [doi: 10.1007/s13177-021-00291-0]
                 [87]   Li QK, Wang ZY, Wang WJ, Zeng C, Li GF, Yuan Q, Cheng B. An adaptive time budget adjustment strategy based on a take-over
                      performance model for passive fatigue. IEEE Trans. on Human-machine Systems, 2022, 52(5): 1025–1035. [doi: 10.1109/THMS.2021.
                      3121665]
                 [88]   Xu  LL.  Research  on  takeover  safety  and  takeover  cognitive  model  for  level  3  automated  driving  [Ph.D.  Thesis].  Dalian:  Dalian
                      University of Technology, 2024 (in Chinese with English abstract). [doi: 10.26991/d.cnki.gdllu.2024.005712]
                 [89]   Köhn T, Gottlieb M, Schermann M, Krcmar H. Improving take-over quality in automated driving by interrupting non-driving tasks. In:
                      Proc. of the 24th Int’l Conf. on Intelligent User Interfaces. Marina del Ray: ACM, 2019. 510–517. [doi: 10.1145/3301275.3302323]
                 [90]   Zhou ZY, Chai C, Yin WR, Shi XP. Developing and evaluating an human-automation shared control takeover strategy based on human-
                      in-the-loop driving simulation. arXiv:2103.06700, 2021.
                 [91]   Fang ZW, Wang JX, Liang JH, Yan YJ, Pi DW, Zhang H, Yin GD. Authority allocation strategy for shared steering control considering
                      human-machine mutual trust level. IEEE Trans. on Intelligent Vehicles, 2024, 9(1): 2002–2015. [doi: 10.1109/TIV.2023.3300152]
                 [92]   Lee JD, See KA. Trust in automation: Designing for appropriate reliance. Human Factors, 2004, 46(1): 50–80. [doi: 10.1518/hfes.46.1.
                      50.30392]
                 [93]   Hoff KA, Bashir M. Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 2015, 57(3):
                      407–434. [doi: 10.1177/0018720814547570]
                 [94]   Gebru B, Zeleke L, Blankson D, Nabil M, Nateghi S, Homaifar A, Tunstel E. A review on human–machine trust evaluation: Human-
                      centric and machine-centric perspectives. IEEE Trans. on Human-machine Systems, 2022, 52(5): 952–962. [doi: 10.1109/THMS.2022.
                      3144956]
                 [95]   Schoeller F, Miller M, Salomon R, Friston KJ. Trust as extended control: Human-machine interactions as active inference. Frontiers in
                      Systems Neuroscience, 2021, 15: 669810. [doi: 10.3389/fnsys.2021.669810]
                 [96]   Lin H, Han JT, Wu PP, Wang JY, Tu J, Tang H, Zhu LN. Machine learning and human-machine trust in healthcare: A systematic
                      survey. CAAI Trans. on Intelligence Technology, 2024, 9(2): 286–302. [doi: 10.1049/cit2.12268]
                 [97]   Razin YS, Feigh KM. Converging measures and an emergent model: A meta-analysis of human-machine trust questionnaires. ACM
                      Trans. on Human-Robot Interaction, 2024, 13(4): 58. [doi: 10.1145/3677614]
                 [98]   Manchon JB, Bueno M, Navarro J. Calibration of trust in automated driving: A matter of initial level of trust and automated driving
                      style? Human Factors, 2023, 65(8): 1613–1629. [doi: 10.1177/00187208211052804]
                 [99]   Sun LS, Cheng ZY, Kong DW, Xu Y, Wen SW, Zhang KY. Modeling and analysis of human-machine mixed traffic flow considering
                      the influence of the trust level toward autonomous vehicles. Simulation Modelling Practice and Theory, 2023, 125: 102741. [doi: 10.
                      1016/j.simpat.2023.102741]
                 [100]   Kok BC, Soh H. Trust in robots: Challenges and opportunities. Current Robotics Reports, 2020, 1(4): 297–309. [doi: 10.1007/s43154-
                      020-00029-y]
                 [101]   Juvina I, Collins MG, Larue O, Kennedy WG, De Visser E, De Melo C. Toward a unified theory of learned trust in interpersonal and
                      human-machine interactions. ACM Trans. on Interactive Intelligent Systems (TiiS), 2019, 9(4): 24. [doi: 10.1145/3230735]
                 [102]   Ekman F, Johansson M, Sochor J. Creating appropriate trust in automated vehicle systems: A framework for HMI design. IEEE Trans.
                      on Human-machine Systems, 2018, 48(1): 95–101. [doi: 10.1109/THMS.2017.2776209]
                 [103]   Muir BM. Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics,
                      1994, 37(11): 1905–1922. [doi: 10.1080/00140139408964957]
                 [104]   Liu J, Marriott K, Dwyer T, Tack G. Increasing user trust in optimisation through feedback and interaction. ACM Trans. on Computer-
                      human Interaction, 2023, 29(5): 42. [doi: 10.1145/3503461]
                 [105]   Hu C, Huang SW, Zhou Y, Ge SC, Yi BL, Zhang X, Wu XD. Dynamic and quantitative trust modeling and real-time estimation in
                      human-machine co-driving process. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 106: 306–327. [doi: 10.
                      1016/j.trf.2024.08.001]
                 [106]   Li HM, Liang MX, Niu K, Zhang YQ. A human-machine trust evaluation method for high-speed train drivers based on multi-modal
                      physiological  information.  Int’l  Journal  of  Human-computer  Interaction,  2025,  41(4):  2659–2676.  [doi:  10.1080/10447318.2024.
                      2327188]
                 [107]   Ding S, Pan X, Hu LH, Liu LZ. A new model for calculating human trust behavior during human-AI collaboration in multiple decision-
   344   345   346   347   348   349   350   351   352   353   354