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为探究隧道不同环境自解释型设计下驾驶员的情境意识状态，从高速公路隧道多维环境特征出发，将隧道划分为可变段、过渡段和不变段，通过分析各功能区段内驾驶员的情境意识状态和图式关系，建立情境意识水平预测模型。采用3D Max软件构建仿真实验场景，选取15名驾驶员开展不同隧道环境自解释型设计场景下的驾驶模拟试验，利用眼动仪、心电仪等采集驾驶员的生理、心理及车辆运行状态数据，计算各方案下驾驶员情境意识水平预测值，最后得出试验组中较符合驾驶员情境意识的方案。研究结果表明：渐变组中驾驶员情境意识水平最高的方案是边坡图案倾斜+黄色渐变彩色路面+蓝-白韵律侧墙，比普通隧道场景下情境意识水平预测值增加了1.181倍；标线组中驾驶员情境意识水平最高的方案是边坡图案倾斜+纵向标线彩色路面+蓝-白韵律侧墙，比普通隧道场景下情境意识水平预测值增加了1.256倍。隧道路段驾驶员情境意识图式和预测模型可为今后隧道环境自解释型设计提供技术参考。
Abstract：In order to explore the situational awareness state of drivers under the self-explaining design of different tunnel environments, starting from the multi-dimensional environmental characteristics of expressway tunnels, the tunnel is divided into variable sections, transition sections and invariant sections. Through analyzing the situational awareness state and schematic relationships of drivers in each functional section, a predictive model of situational awareness level is established. And 3D Max software is used to construct a simulation experiment scene, by selecting 15 drivers to carry out driving simulation experiments under different tunnel environment self-explaining design scenarios. Using eye trackers, electrocardiographs, etc, the driver′s physiological, psychological, and vehicle operating status data are collected, and the predicted values of driver′s situational awareness level are calculated for each scheme. Finally, the scheme in the experimental group that best fits the driver′s situational awareness is obtained. The research results show that the scheme with the highest level of situational awareness of drivers in the gradient group is slope pattern tilted+yellow gradient color road surface+blue-white rhythm side wall, which increases the predicted value of situational awareness level by 1.181 times compared with the ordinary tunnel scene; The scheme with the highest level of situational awareness among drivers in the group is slope pattern slope+longitudinal marking color road+blue-white rhythm side wall, which increases the predicted value of situational awareness level by 1.256 times compared with the ordinary tunnel scene. The situational awareness schema and prediction model for tunnel drivers can provide technical reference for future self-explaining design of tunnel environments.
[1] 交通运输部.202年交通运输行业发展统计公报[N].中国交通报.2021-05-19(2).
[2] 胡月琦.高速公路隧道路段驾驶人心智游移及心理负荷与安全风险研究[D].西安：长安大学，2021.
[3] 闵强.山区高速公路隧道路段安全问题及对策研究[J].湖南交通科技，2022,48(1):166-170.
[4] 杨翊匙.需求导向道路交通设计的自解释性研究[D].北京：中国人民公安大学，2019.
[5] KABER D,JIN S,ZAHABI M,et al.The effect of driver cognitive abilities and distractions on situation awareness and performance under hazard conditions[J].Transportation Research.Part F,Traffic Psychology and Behaviour,2016,42:177-194.
[6] D′ANIELLO G,LOIA V,ORCIUOLI F.Adaptive goal selection for improving situation awareness:the fleet management case study[J].Procedia Computer Science,2017,109:529-536.
[7] TAYLOR B,CHEKALUK E,IRWIN J.Reading the situation:the relationship between dyslexia and situational awareness for road sign information[J].Transportation Research Part F:Traffic Psychology and Behaviour,2016,36:6-13.
[8] XING Y L,PARK S,AKASH K,et al.Investigating the impact of context and environment on driver's situation awareness[J].Proceedings of the Human Factors and Ergonomics Society Annual Meeting,2022,66(1):335-339.
[9] HOFBAUER M,KUHN C B,PüTTNER L,et al.Measuring driver situation awareness using region-of-interest prediction and eye tracking[C]//2020 IEEE International Symposium on Multimedia (ISM).Naples,Italy:IEEE,2020:91-95.
[10] YANG Y Q,CHEN M F,WU C X,et al.Structural equation modeling of drivers' situation awareness considering road and driver factors[J].Frontiers in Psychology,2020,11:1601.
[11] 陈晨.驾驶场景视觉注意的性别差异——基于眼动的研究[D].西安：陕西师范大学，2021.
[12] 冯传宴，完颜笑如，刘双，等.不同脑力负荷水平下的情境意识研究[J].西北工业大学学报，2020,38(3):610-618.
[13] 肖家琛.认知负荷对机动车驾驶人驾驶绩效的影响：情境意识和风险感知的作用[D].保定：河北大学，2021.
[14] 何文强.高速公路互通区指路标志对驾驶员情境意识的影响研究[D].福州：福州大学，2018.
[15] 陈梅凤.机动车驾驶员情境意识建模及实证研究[D].福州：福州大学，2020.
[16] 杨艳群，陈梅凤，郑新夷，等.基于扎根理论的驾驶员情境意识影响因素研究[J].交通工程，2020,20(2):12-18.
[17] 赵丹婷，马骏，安蕾冰.驾驶情景构建中期望因素及其作用机制[J].山东交通科技，2021(1):144-147.
[18] 杨春风，陈曦.自解释道路理念探析[J].交通标准化，2014,42(7):43-46,53.
[19] 招商局重庆交通可研设计院有限公司.公路隧道设计规范：第一册土建工程：JTG 3370.1—2018[S].北京：人民交通出版社，2018.
[20] 招商局重庆交通可研设计院有限公司.公路隧道照明设计细则：JTG/T D70/2-01—2014[S].北京：人民交通出版社，2014.
[21] ENDSLEY M R.Toward a theory of situation awareness in dynamic systems[J].Human Factors,1995,37(1):32-64.
[22] WALKER G H,STANTON N A,KAZI T A,et al.Does advanced driver training improve situational awareness?[J].Applied Ergonomics,2009,40(4):678-687.
[23] KIRLIK A,STRAUSS R.Situation awareness as judgment I:Statistical modeling and quantitative measurement[J].International Journal of Industrial Ergonomics,2006,36(5):463-474.
[24] 高峰，张龙潇，刘汉银，等.毗邻隧道连接段进口处视觉变化及合理照度研究[J].公路工程，2021,46(3):112-117.
基本信息:
DOI：10.19782/j.cnki.1674-0610.2024.05.004
中图分类号:U491.254;U452
引用信息:
[1]万利,袁华智,王玉莹,等.隧道环境自解释型设计下驾驶员情境意识状态分析[J].公路工程,2024,49(05):25-33.DOI:10.19782/j.cnki.1674-0610.2024.05.004.
基金信息:
国家自然科学基金资助项目(51978069,52362050); 山东省交通科技项目(2022-KJ-044); 中央高校基本科研业务费专项资金项目(300102342202); 兰州理工大学红柳优秀青年人才计划项目(062213)
2024-10-20
2024-10-20