Page 345 - 《软件学报》2026年第3期
P. 345

1308                                                       软件学报  2026  年第  37  卷第  3  期


                    (5) 伦理与法规框架的构建: 开展前瞻性的人机共驾责任界定框架研究, 明确动态接管过程中的责任边界; 建
                 立以人为本的伦理设计准则, 确保技术的发展与人类的价值观和安全诉求相一致.
                    展望未来, 通过攻克上述科学与技术难题, 人机共驾系统将最终演进为一个能理解、会适应、可信任的“智能
                 副驾”, 推动人机协同实现从“机械协作范式”到“认知融合范式”的根本性转变, 最终构建高效、自然的人机认知共
                 同体.

                 References
                  [1]   Kenesei Z, Ásványi K, Kökény L, Jászberényi M, Miskolczi M, Gyulavári T, Syahrivar J. Trust and perceived risk: How different
                      manifestations affect the adoption of autonomous vehicles. Transportation Research Part A: Policy and Practice, 2022, 164: 379–393.
                      [doi: 10.1016/j.tra.2022.08.022]
                  [2]   Jui JJ, Hettiarachchi IT, Mohajer N. Need for trust calibration in takeover request performance in Level 3 automated vehicles. In: Proc.
                      of the 17th Int’l Conf. on Automotive User Interfaces and Interactive Vehicular Applications. New York: ACM, 2025. 296–306. [doi: 10.
                      1145/3744333.3747816]
                  [3]   Pakdamanian E, Hu EZ, Sheng SL, Kraus S, Heo S, Feng L. Enjoy the ride consciously with CAWA: Context-aware advisory warnings
                      for automated driving. In: Proc. of the 14th Int’l Conf. on Automotive User Interfaces and Interactive Vehicular Applications. Seoul:
                      ACM, 2022. 75–85. [doi: 10.1145/3543174.3546835]
                  [4]   Liu WM, Li QK, Wang ZY, Wang WJ, Zeng C, Cheng B. Takeover directly or gradually? Comparison of single stage and dual stage
                      human-machine interface on drivers’ visual behaviors and subjective ratings over cognitive demand, motoric demand, and time demand.
                      In: Proc. of the 4th Int’l Conf. on Big Data Engineering. Beijing: ACM, 2022. 60–70. [doi: 10.1145/3538950.3538959]
                  [5]   Chen FC, Lu GQ, Lin QF, Zhang HD, Ma SQ, Liu DZ, Song HJ. Review of drivers’ takeover behavior in conditional automated driving.
                      Journal of Jilin University (Engineering and Technology Edition), 2025, 55(2): 419–433 (in Chinese with English abstract). [doi: 10.
                      13229/j.cnki.jdxbgxb.20231033]
                  [6]   Yan  LX,  Feng  JP,  Guo  JH,  Gong  YK.  Analysis  of  characteristics  of  the  takeover  behavior  of  co-driving  intelligent  vehicles  under
                      different dangerous scenarios. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(3): 683–691 (in Chinese with
                      English abstract). [doi: 10.13229/j.cnki.jdxbgxb.20220580]
                  [7]   Guo BC, Luo GF, Jin LS, Shi YW, Han ZT, Zhang HY. Impact of risk scenario-driven secondary task driving behavior on takeover
                      performance. Journal of Tongji University (Natural Science), 2024, 52(6): 875–885 (in Chinese with English abstract). [doi: 10.11908/j.
                      issn.0253-374x.24135]
                  [8]   Tu HZ, Liu JQ, Wei YT, Wang WJ, Guo JQ, Wang M. Autonomous driving road test risk scenario inference based on counterfactual
                      analysis. Journal of Tongji University (Natural Science), 2025, 53(2): 223–232 (in Chinese with English abstract). [doi: 10.11908/j.issn.
                      0253-374x.23406]
                  [9]   Wang WJ, Li QK, Zeng C, Li GF, Zhang JL, Li SB, Cheng B. Review of take-over performance of automated driving: Influencing
                      factors, models, and evaluation methods. China Journal of Highway and Transport, 2023, 36(9): 202–224 (in Chinese with English
                      abstract). [doi: 10.19721/j.cnki.1001-7372.2023.09.017]
                 [10]   Lu ZJ, Happee R, Cabrall CDD, Kyriakidis M, de Winter JCF. Human factors of transitions in automated driving: A general framework
                      and literature survey. Transportation Research Part F: Traffic Psychology and Behaviour, 2016, 43: 183–198. [doi: 10.1016/j.trf.2016.10.
                      007]
                 [11]   Lu GQ, Zhao PY, Wang ZJ, Lin QF. Impact of visual secondary task on young drivers’ take-over time in automated driving. China
                      Journal of Highway and Transport, 2018, 31(4): 165–171 (in Chinese with English abstract). [doi: 10.3969/j.issn.1001-7372.2018.04.
                      020]
                 [12]   Li MF, Feng ZX, Zhang WH, Li JY. Study on driver’s visual transfer characteristics during the takeover process of human-computer co-
                      driving mode. Automotive Engineering, 2024, 46(5): 795–804 (in Chinese with English abstract). [doi: 10.19562/j.chinasae.qcgc.2024.
                      05.006]
                 [13]   Ma  YL,  Lu  J,  Zhu  JY,  Han  XX.  Take-over  performance  prediction  under  different  cognitive  loads  of  non-driving  tasks  in  highly
                      automated  driving.  Automotive  Engineering,  2023,  45(12):  2330–2337,  2329  (in  Chinese  with  English  abstract).  [doi:  10.19562/j.
                      chinasae.qcgc.2023.12.015]
                 [14]   Scatturin L, Erbach R, Baumann M. Cognitive psychological approach for unraveling the take-over process during automated driving.
                      In: Proc. of the 11th Int’l Conf. on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proc. Utrecht: ACM,
                      2019. 215–220. [doi: 10.1145/3349263.3351501]
                 [15]   Wörle J, Metz B, Baumann M. Sleep inertia in automated driving: Post-sleep take-over and driving performance. Accident Analysis &
                      Prevention, 2021, 150: 105918. [doi: 10.1016/j.aap.2020.105918]
   340   341   342   343   344   345   346   347   348   349   350