Transportation Research Part F-Traffic Psychology and Behaviour最新文献_第6页

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-27 DOI: 10.1016/j.trf.2025.103499

Byungju Kim , Yanbin Wu , Ken Kihara , Yuji Takeda , Takatsune Kumada

Age-related changes in driving performance have long been a critical issue in traffic safety. However, it remains unclear whether such differences occur during curve negotiation, particularly in the approach phase preceding curve entry. This study examined age-related changes that emerge in this context and investigated whether driver assistance systems can mitigate these differences. Thirty-six participants (mean age = 44.3 years, SD = 17.1) completed a 30-min simulated drive under three conditions: Manual, Lane Centering (LC), and Adaptive Cruise Control (ACC). In the Manual condition, participants maintained a speed of 60 km/h and kept the vehicle centered in the lane. In the LC condition, lateral support was provided, ACC controlled speed, and participants managed the remaining operations. Results showed that, under the Manual condition, increasing age was associated with initiating curve preparation closer to the entry point and with greater variability and magnitude of steering. Under ACC, curve preparation began earlier, as indicated by an earlier Start Point of Steering Wheel Angle (SPSWA), although age-related steering patterns remained unchanged. No significant age-related differences were found in accelerator pedal use under the Manual condition, and neither performance differences nor age-related patterns were observed in the LC condition. These findings indicate that age-related changes are evident in steering. ACC promotes earlier curve entry preparation but does not compensate for age-related steering differences. LC showed limited effectiveness across age groups. Trial-level analyses confirmed that automation did not differentially affect age-related patterns in driver control behavior. Overall, the findings underscore the need for driver assistance systems that more effectively support age-related changes in lateral control during curve negotiation.

与年龄相关的驾驶表现变化一直是交通安全的关键问题。然而，尚不清楚这种差异是否发生在曲线协商过程中，特别是在进入曲线之前的接近阶段。这项研究调查了在这种情况下出现的与年龄相关的变化，并调查了驾驶员辅助系统是否可以减轻这些差异。36名参与者（平均年龄= 44.3岁，SD = 17.1）在手动、车道定心（LC）和自适应巡航控制（ACC）三种条件下完成了30分钟的模拟驾驶。在手动条件下，参与者保持60公里/小时的速度，并保持车辆在车道中央。在LC条件下，提供横向支持，ACC控制速度，参与者管理剩余的操作。结果表明，在手动工况下，年龄的增加与起始曲线准备更接近入口点有关，并且与更大的变异性和转向幅度有关。在ACC组，曲线准备开始得更早，从方向盘角度起始点（SPSWA）可以看出，尽管与年龄相关的转向模式保持不变。手动工况下的加速踏板使用没有明显的年龄相关差异，LC工况下的性能差异和年龄相关模式均未观察到。这些发现表明，与年龄相关的变化在驾驶方面是明显的。ACC促进早期入弯准备，但不能弥补与年龄相关的转向差异。LC在不同年龄组的有效性有限。试验水平的分析证实，自动驾驶对驾驶员控制行为的年龄相关模式没有差异影响。总的来说，研究结果强调了驾驶员辅助系统的必要性，该系统可以更有效地支持与年龄相关的转弯时横向控制的变化。

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引用次数: 0

Optimizing urban road cross-section’s design to accommodate safe autonomous vehicle-cyclist interactions: A bicycle simulator study 优化城市道路横截面设计，以适应安全的自动驾驶汽车-骑自行车者互动：自行车模拟器研究

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-27 DOI: 10.1016/j.trf.2025.103488

Amira Hammami, Attila Borsos

The introduction of autonomous vehicles (AVs) in urban environments where cycling activity is present has raised the need to investigate potential modifications to urban roads, not only from the perspective of AVs but also from the perspective of cyclists. This study aims to investigate the effect of different road design characteristics and varying traffic penetration rates of AVs, using a bicycle simulator study. 50 participants assessed their perceived level of safety, comfort and stress in 11 randomized scenarios. The scenarios involve a design with sharrows and four designs with separated cycling lanes (with two different cycling lane width and two pavement painting options), with 3 AV traffic penetration rates (TPR): 0 %, 50 %, and 100 %. A series of cumulative link mixed models (CLMM) was estimated to analyze the impact of design characteristics and TPRs on cyclist perceptions. The results revealed that the implementation of AVs in shared road scenarios did not improve cyclist safety. On the contrary, it has significantly reduced the perceived level of comfort and has significantly increased the perceived level of stress. However, in separated cycling lane designs, the presence of AVs was found to positively affect cyclist perceptions, although this impact was not significant. Furthermore, the study revealed that the most important factor that affects perceptions of safety, comfort, and stress is the separation between traffic and cycling lanes.

在城市环境中引入自动驾驶汽车（AVs），人们不仅需要从自动驾驶汽车的角度，还需要从骑自行车的人的角度来调查城市道路的潜在修改。本研究旨在通过自行车模拟器研究不同道路设计特征和不同交通渗透率对自动驾驶汽车的影响。50名参与者在11个随机场景中评估了他们对安全、舒适和压力的感知水平。这些场景包括一个有车道的设计和四个有独立自行车道的设计（有两种不同的自行车道宽度和两种路面粉刷选项），自动驾驶汽车的交通渗透率（TPR）为0%、50%和100%。通过一系列累积链接混合模型（CLMM）来分析设计特征和tpr对骑行者感知的影响。结果表明，在共享道路场景中实施自动驾驶汽车并没有提高骑自行车者的安全性。相反，它显著降低了感知的舒适水平，显著增加了感知的压力水平。然而，在独立的自行车道设计中，自动驾驶汽车的存在对骑车人的感知产生了积极的影响，尽管这种影响并不显著。此外，研究还表明，影响人们对安全、舒适和压力感知的最重要因素是交通和自行车道之间的分隔。

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引用次数: 0

Pedestrian crossing strategy while interacting with multiple autonomous vehicles in local street intersections: Effects of road infrastructure 与多辆自动驾驶汽车在当地街道交叉口交互时的行人过街策略：道路基础设施的影响

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-26 DOI: 10.1016/j.trf.2025.103503

Duc Trung Luu , Tiju Baby , Jieun Lee , Tatsuru Daimon , Seul Chan Lee

Crossing strategy refers to pedestrian objectives in choosing crossing patterns and their pace. To the best of our knowledge, there remains a deficiency in literature regarding pedestrian crossing strategies when interacting with autonomous vehicles (AVs). This study investigated the effect of various physical road infrastructures on pedestrian crossing strategies while interacting with multiple AVs. The effects of three infrastructures in a typical local street on the crossing strategies of pedestrians were identified using a structural equation model based on a stimulus–organism–response (SOR) framework. The stimulus included sidewalk, crosswalk, and/or legal on-street parking designed virtually in a four-lane local intersection, where frequent daily pedestrian–AV interactions occurred in a mixed neighborhood. The organism dimension of pedestrians was measured in terms of situation awareness (SA) and perceived risk (PR) when interacting with multiple AVs. Pedestrian crossing strategies, including their intended crossing patterns and speeds, were identified in the response dimension. Based on experimental data from 82 university students, the findings revealed that sidewalk and legal on-street parking significantly affected SA with coefficients of 0.155 and − 0.079, respectively, whereas the crosswalk had a remarkable association with PR by a coefficient of −0.098. In addition, there was a trade-off relationship between pedestrian patterns and speed (coefficient of −0.197) when interacting with multiple AVs. Our research on pedestrians interacting with multiple AVs provides novel insights to enhance the understanding of pedestrian crossing strategies and establish a comparison with the realities of crowded local roadways. These insights expand our knowledge of actual pedestrian crossing behaviors in the AV environment and support the production of safer street design guidelines.

人行横道策略是指行人选择人行横道的方式和速度的目标。据我们所知，在与自动驾驶汽车（AVs）互动时，关于行人过马路策略的文献仍然缺乏。本研究探讨了不同物理道路基础设施在与多辆自动驾驶汽车交互时对行人过马路策略的影响。采用基于刺激-生物-反应（SOR）框架的结构方程模型，研究了典型街道中三种基础设施对行人过马路策略的影响。刺激措施包括人行道、人行横道和/或合法的街道停车位，这些停车位实际上设计在四车道的当地十字路口，在混合社区中，行人和自动驾驶汽车每天频繁互动。以行人与多辆自动驾驶汽车互动时的态势感知（SA）和感知风险（PR）为指标，测量行人的机体维度。在响应维度上确定了行人过马路的策略，包括预期过马路的方式和速度。基于82名大学生的实验数据，研究发现，人行道和合法的路边停车对SA的影响显著（系数分别为0.155和- 0.079），而人行横道对PR的影响显著（系数为- 0.098）。此外，当与多辆自动驾驶汽车交互时，行人模式与速度之间存在权衡关系（系数为- 0.197）。我们对行人与多辆自动驾驶汽车互动的研究提供了新的见解，以增强对行人过马路策略的理解，并与拥挤的当地道路现实进行比较。这些见解扩展了我们对自动驾驶环境中实际行人过马路行为的了解，并支持制定更安全的街道设计指南。

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引用次数: 0

Wrong-way riding behavior among e-bike riders: The roles of willingness and intention 电动自行车骑行者的错误骑行行为：意愿和意图的作用

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-26 DOI: 10.1016/j.trf.2025.103500

Hongjun Cui , Mingzheng Zhang , Minqing Zhu , Xiaotao Yuan

Wrong-way riding (WWR) among e-bike riders, as a typical traffic violation, has become a significant contributor to road crashes. However, most prior studies have focused separately on sociodemographic and environmental factors of WWR among e-bike riders, leaving the psychosocial factors and cognitive decision-making processes largely underexplored. To fill this gap, this study adopted the theory of planned behavior (TPB), the prototype willingness model (PWM), and the integrated model of the TPB and the PWM to investigate WWR behavior among e-bike riders. Structural equation modeling (SEM) was employed to compare the explanatory power of the three models and to identify the key psychological predictors and cognitive decision-making pathways associated with WWR behavior. Data were collected via an online survey of 709 e-bike users in China. The results showed that the TPB, the PWM, and the integrated model all effectively explained WWR behavior among e-bike riders, with the integrated model demonstrating the strongest explanatory power. Both behavioral intention and behavioral willingness significantly influenced WWR behavior, with behavioral willingness exerting a stronger effect. These findings suggest that WWR behavior is predominantly driven by social reactive decision-making rather than reasoned decision-making. Descriptive norms were the strongest predictor of both behavioral intention and behavioral willingness. Attitudes, perceived behavioral control, and prototype perception were also significant predictors of WWR behavior among e-bike riders. Finally, based on the findings of this study, specific intervention strategies were proposed to reduce the incidence of WWR among e-bike riders, aiming to enhance e-bike traffic safety.

电动自行车逆行是典型的交通违规行为，已成为道路交通事故的重要原因。然而，以往的研究大多集中在社会人口学和环境因素上，而对社会心理因素和认知决策过程的探索不足。为了填补这一空白，本研究采用计划行为理论（TPB）、原型意愿模型（PWM）以及原型意愿模型与原型意愿模型的集成模型来研究电动自行车骑行者的WWR行为。采用结构方程模型（SEM）比较三种模型的解释能力，并确定与WWR行为相关的关键心理预测因子和认知决策途径。数据是通过对709名中国电动自行车用户的在线调查收集的。结果表明，TPB、PWM和综合模型均能有效解释电动自行车骑行者的WWR行为，其中综合模型的解释力最强。行为意向和行为意愿均显著影响员工WWR行为，其中行为意愿的影响更强。这些发现表明，WWR行为主要是由社会反应性决策而不是理性决策驱动的。描述性规范是行为意向和行为意愿的最强预测因子。态度、感知行为控制和原型感知也是电动自行车骑行者WWR行为的显著预测因子。最后，根据研究结果，提出了具体的干预策略，以降低电动自行车骑行者的WWR发生率，以提高电动自行车的交通安全性。

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引用次数: 0

Eye, steering, and hands on wheel behaviors indicating driver engagement in level 2 driving 在2级驾驶中，眼睛、方向盘和手在方向盘上的行为表明驾驶员参与驾驶

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-26 DOI: 10.1016/j.trf.2025.103501

Emma Tivesten, Thomas Streubel, Mikael Ljung Aust

Advanced driver assistance systems that simultaneously perform lateral and longitudinal control reduce the need for driver input, potentially leading to driver disengagement. As these systems become more capable of performing most of the operational control, drivers tend to increase eyes off road, hands off wheel, and they may be unprepared to act in situations that exceed the system's capabilities.

In this study, we analyzed the behaviors of 54 participants using a level 2 system on a test track. Drivers were considered disengaged if they had a late or absent response to a conflict at the end of the drive, resulting in a crash or near-crash. Several behaviors were associated with increased risk of disengagement, including long off-path glances, frequent visual time-sharing, gaze concentration, lack of driver steering input, and hands off wheel during uneventful driving. In contrast, continuous engagement in steering appeared to promote driver engagement, even among participants who exhibited suboptimal gaze behavior.

These findings suggest that combining metrics of steering activity and gaze behavior provides a more comprehensive assessment of driver engagement. This insight can inform the design of driver monitoring and engagement strategies in level 2 driving systems.

同时执行横向和纵向控制的先进驾驶员辅助系统减少了驾驶员输入的需求，可能导致驾驶员脱离驾驶。随着这些系统越来越有能力完成大部分的操作控制，驾驶员往往会把目光从道路上移开，双手从方向盘上移开，在超出系统能力的情况下，他们可能没有准备好采取行动。在这项研究中，我们分析了54名参与者在测试轨道上使用二级系统的行为。如果司机在驾驶结束时对冲突反应迟缓或缺席，导致撞车或差点撞车，就会被认为是心不在焉。有几种行为会增加脱离驾驶的风险，包括长时间的视线偏离、频繁的视觉共享、注意力集中、缺乏驾驶员的方向盘输入，以及在平稳驾驶时把手离开方向盘。相比之下，持续专注于驾驶似乎促进了驾驶员的投入，即使在表现出次优凝视行为的参与者中也是如此。这些发现表明，结合驾驶活动和凝视行为的指标可以更全面地评估驾驶员的参与程度。这种见解可以为二级驾驶系统的驾驶员监控和参与策略的设计提供信息。

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引用次数: 0

Identification of factors influencing pedestrians' red-light crossing behavior based on interpretable machine learning 基于可解释机器学习的行人闯红灯行为影响因素识别

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-25 DOI: 10.1016/j.trf.2025.103487

Kun Wang , Rensu Zhou , Shuo Yang , Cheng Wang , Jing Liu , Lu Wei , LiangXu

Pedestrians' crossing the street while running a red light are a significant factor contributing to traffic accidents at intersections. Traditional models fail to capture the complex, multifactorial nonlinearities and interactions involved in this behavior due to their limited linear analytical power, while machine learning models suffer from interpretability issues. To address this, an analytical framework that combines data-driven machine learning algorithms with emerging interpretability techniques was proposed, aiming to reveal the complex, nonlinear effects and relative importance of factors influencing pedestrian red-light-crossing behavior. Empirical video data from five signalized intersections in Hefei, China were used to compare the modeling and prediction performance of four methods: logistic regression, K-nearest neighbors, support vector machine, and extreme gradient boosting (XGBoost). Shapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE) were employed to evaluate the key factors influencing pedestrians' decisions to cross at a red light. The results show that the XGBoost model outperforms the other algorithms in capturing the complex relationships among influencing factors and accurately identifying red-light-running behavior. Quantitative analysis of feature importance reveals that traffic volume is the most influential predictor, followed by pedestrian walking speed, red-light duration, conformity behavior, and age. This study overcomes the linear constraints of traditional regression models and provides a theoretical foundation for optimizing traffic management and developing intelligent law enforcement strategies.

行人闯红灯过马路是造成十字路口交通事故的重要因素。传统模型由于其有限的线性分析能力而无法捕捉到这种行为中涉及的复杂、多因素非线性和相互作用，而机器学习模型则存在可解释性问题。为了解决这个问题，提出了一个将数据驱动的机器学习算法与新兴的可解释性技术相结合的分析框架，旨在揭示影响行人闯红灯行为的因素的复杂性、非线性效应和相对重要性。利用合肥五个信号交叉口的经验视频数据，比较了逻辑回归、k近邻、支持向量机和极限梯度增强（XGBoost）四种方法的建模和预测性能。采用Shapley加性解释法（SHAP）和累积局部效应法（ALE）对影响行人闯红灯决策的关键因素进行了评价。结果表明，XGBoost模型在捕捉影响因素之间的复杂关系和准确识别闯红灯行为方面优于其他算法。特征重要性的定量分析表明，交通量是最具影响力的预测因子，其次是行人行走速度、红灯持续时间、从众行为和年龄。该研究克服了传统回归模型的线性约束，为优化交通管理和制定智能执法策略提供了理论基础。

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引用次数: 0

Driving under time pressure: Driver state and behavior changes with limited time savings in complex road networks - A naturalistic time-incentive study with young male drivers 时间压力下的驾驶：复杂道路网络中有限时间节省下的驾驶员状态和行为变化——对年轻男性驾驶员的自然时间激励研究

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-24 DOI: 10.1016/j.trf.2025.103483

Yanqing Yao , Wuyang Chen , Xiaoyu Zhuang , Chenyu Xu , Doyeon Lee , Xiaoou Yang , Jie Wang , Shijian He

When driving under time pressure, drivers often adjust their behavior to save travel time. Understanding these changes is important for evaluating their potential impacts on traffic safety and travel efficiency. This study examines how time pressure affects driving behavior, physiological responses, and travel time in complex urban road environments. Thirty-two young drivers completed two naturalistic driving tasks representing low and high time-pressure conditions on routes containing traffic-light-dense and non-dense segments. The driving behavior (speed, overspeeding frequency, and lane-changing frequency), physiological indicators (heart rate and skin conductance level), and travel time were recorded. The results show that time pressure led to significantly more assertive driving behaviors, with higher speeds and increased overspeeding and lane-changing frequency under high time pressure. By contrast, no statistically significant differences were observed in both the heart rate and skin conductance levels across the roadway segments. The effects of time pressure on the travel time were highly context dependent: no meaningful time savings occurred on traffic-light-dense segments, whereas small but measurable reductions were achieved on non-dense segments. These findings indicate that although time pressure reliably intensifies driving behavior, actual efficiency gains are limited and strongly constrained by roadway signal density. This evidence supports efforts in traffic safety policy and driver education to recalibrate drivers’ expectations regarding the effectiveness of assertive driving under time pressure.

在时间紧迫的情况下开车时，司机往往会调整自己的行为来节省出行时间。了解这些变化对于评估其对交通安全和出行效率的潜在影响非常重要。本研究探讨了在复杂的城市道路环境中，时间压力如何影响驾驶行为、生理反应和行驶时间。32名年轻司机完成了两项自然驾驶任务，分别代表低时间压力和高时间压力条件，其中包括交通轻密度和非密集路段。记录受试者的驾驶行为（车速、超速频率、变道频率）、生理指标（心率、皮肤电导水平）和行驶时间。结果表明，时间压力显著提高了司机的自信驾驶行为，在高时间压力下，司机的车速更高，超速和变道频率增加。相比之下，在不同路段的心率和皮肤电导水平上没有统计学上的显著差异。时间压力对旅行时间的影响是高度依赖于环境的：在交通轻密度路段没有明显的时间节省，而在非密集路段则实现了小但可测量的减少。这些发现表明，虽然时间压力确实会增强驾驶行为，但实际效率的提高是有限的，并且受到道路信号密度的强烈约束。这一证据支持了交通安全政策和驾驶员教育方面的努力，以重新调整驾驶员对时间压力下自信驾驶有效性的期望。

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引用次数: 0

Driving the shift to electric motorcycles: the role of AI trust and AI benefits 推动向电动摩托车的转变：人工智能信任和人工智能效益的作用

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-20 DOI: 10.1016/j.trf.2025.103484

Duy Quy Nguyen-Phuoc , Thao Nhi Ho-Mai , Thao Phuong Thi Nguyen , Nhat Dinh Quang Vo , Tuan Trong Luu , Diep Ngoc Su

Motorcycles dominate urban transport in Southeast Asia but significantly contribute to air pollution and health risks. Government efforts to promote electric motorcycles (EMs) have been extensive, yet consumer switch rates continue to lag behind expectations. Currently, AI plays a transformative role in the development, functionality, and optimisation of EMs; however, the influence of beliefs in AI benefits and trust in AI on EM switching intention has not been examined. This study addresses this gap by extending the Value-Belief-Norm theory with these two constructs, incorporating age and gender as moderators. Additionally, perceived value is treated as a second-order construct, consisting of five dimensions that collectively capture its multifaceted nature. Data from Vietnamese respondents were analysed using PLS-SEM. The results show that perceived value directly influences beliefs in EM benefits, AI benefits for EMs, and trust in AI technologies. Trust in AI and personal norms significantly shape the switching intention. This research provides actionable recommendations for policymakers to accelerate EM switching intention and contributes to advancing theoretical discussions on sustainable transport and AI integration in mobility solutions.

摩托车在东南亚的城市交通中占主导地位，但也严重造成空气污染和健康风险。政府大力推广电动摩托车，但消费者的转换率仍然落后于预期。目前，人工智能在新兴市场的开发、功能和优化方面发挥着变革性作用；然而，对人工智能利益的信念和对人工智能的信任对EM转换意愿的影响尚未得到研究。本研究通过将年龄和性别作为调节因素，将价值-信念-规范理论扩展到这两个构念，从而解决了这一差距。此外，感知价值被视为二阶结构，由五个维度组成，共同捕捉其多面性。越南受访者的数据使用PLS-SEM进行分析。结果表明，感知价值直接影响对新兴市场利益的信念、新兴市场的人工智能利益以及对人工智能技术的信任。对人工智能的信任和个人规范显著影响了转换意愿。这项研究为政策制定者提供了可操作的建议，以加速新兴市场的转换意图，并有助于推进可持续交通和人工智能在移动解决方案中的整合的理论讨论。

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引用次数: 0

Investigating the negative emotional intensity and cognitive workload levels of young Novice drivers in different high-risk driving scenarios: A simulated driving study 不同高危驾驶情景下年轻新手驾驶员负性情绪强度和认知负荷水平的模拟驾驶研究

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-20 DOI: 10.1016/j.trf.2025.103493

Weiwei Wang , Zhiqiang Wen , Qizhao Peng , Zihao Zhang , Congge Shi , Ting Wei

Young novice drivers are prone to negative emotions in high-risk situations. These emotions consume limited cognitive resources and raise crash risk. Existing research has not systematically clarified the relationships among driving scenarios, emotion types, emotion intensity, and multi-channel cognitive workload. Accordingly, this study used a driving-simulator experiment to analyze these linkages. We recruited 144 Chinese young novice drivers and used pre-validated video clips to induce neutral, anger, fear, anxiety. Data were collected via the Self-Assessment Manikin (SAM), the Visual-Auditory-Cognitive-Psychomotor (VACP) workload model, and semi-structured interviews. The results showed that: (1) Negative emotions significantly increased cognitive workload in young novice drivers. Anger and fear causd significant instantaneous workload fluctuations, whereas anxiety yielded the highest mean workload. (2) Distinct negative emotions were triggered by specific driving scenarios, which have different stressors (such as security threat, time pressure and environmental complexity). The potential outcome brought by these situational stressors affect the intensity of emotion. (3) Emotion intensity was positively associated with workload level. High-arousal emotions more likely to increase demands on visual, cognitive, and psychomotor resources. Within a unified paradigm, this study delineates the pathway linking driving scenarios, emotion types, emotion intensity, and multi-channel workload. The findings provide evidence for in-vehicle emotion monitoring and environmental-adaptive interventions.

年轻的新手司机在高风险的情况下容易产生负面情绪。这些情绪消耗有限的认知资源，增加崩溃的风险。现有研究尚未系统地阐明驾驶情景、情绪类型、情绪强度与多通道认知负荷之间的关系。因此，本研究使用驾驶模拟器实验来分析这些联系。我们招募了144名中国年轻的新手司机，并使用预先验证的视频片段来诱导中性、愤怒、恐惧和焦虑。数据通过自我评估模型（SAM）、视觉-听觉-认知-精神运动（VACP）工作量模型和半结构化访谈收集。结果表明：(1)负性情绪显著增加了年轻新手驾驶员的认知负荷。愤怒和恐惧会导致显著的瞬时工作量波动，而焦虑会产生最高的平均工作量。(2)不同的驾驶场景会触发不同的负性情绪，这些负性情绪具有不同的应激源（如安全威胁、时间压力和环境复杂性）。这些情境应激源所带来的潜在结果会影响情绪的强度。(3)情绪强度与工作量水平正相关。高唤醒情绪更有可能增加对视觉、认知和精神运动资源的需求。在一个统一的范式中，本研究描绘了驾驶场景、情绪类型、情绪强度和多渠道工作量之间的联系途径。研究结果为车载情绪监测和环境适应性干预提供了依据。

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引用次数: 0

Explicit knowledge of driver monitoring systems changes their effectiveness in reducing visual distraction 对驾驶员监控系统的明确了解改变了它们在减少视觉分心方面的有效性

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour

Pub Date : 2025-12-17 DOI: 10.1016/j.trf.2025.103480

Ina Koniakowsky , Yannick Forster , Frederik Naujoks , Josef F. Krems , Andreas Keinath

Driver monitoring systems (DMS) represent a camera-based countermeasure for visual distraction that detect distracted drivers in real-time and subsequently prompt them to look back on the road. However, the effectiveness of DMS in reducing distraction is still being debated, with studies yielding inconsistent results. A correct understanding of a technological system is a key determinant for its effectiveness in terms of enhancing safety. Therefore, addressing a previously unexplored factor in DMS research, this study investigated how drivers' explicit knowledge about DMS influences system effectiveness in reducing visual distraction. Previous studies showed that drivers' understanding of DMS is incomplete if drivers are not instructed. Therefore, in this study, the drivers' explicit knowledge of DMS was systematically manipulated between participants by providing verbal instructions prior to driving. Three experimental conditions were compared: drivers with explicit knowledge of DMS, drivers without knowledge, and a control group with an inactive DMS (between factor). Glance behavior was compared between the first and repeated interaction with the secondary task to assess visual distraction (within factor). Explicit knowledge significantly reduced the number of glances exceeding 2 s, indicating reduced visual distraction. This effect was prevalent, even after repeated interaction with the DMS. Importantly, mere activation of DMS without instruction did not affect glance behavior. Findings highlight the significant role of explicit knowledge in system effectiveness. The present work contributes to the field of DMS research by investigating drivers' mental model of DMS and deriving methodological and practical implications.

驾驶员监控系统（DMS）代表了一种基于摄像头的视觉分心对策，可以实时检测分心的驾驶员，并随后提示他们回头看路。然而，DMS在减少注意力分散方面的有效性仍在争论中，研究结果不一致。对技术系统的正确理解是其在提高安全性方面的有效性的关键决定因素。因此，为了解决DMS研究中一个以前未被探索的因素，本研究调查了驾驶员对DMS的显性知识如何影响系统在减少视觉分心方面的有效性。以前的研究表明，如果司机没有得到指导，司机对DMS的理解是不完整的。因此，在本研究中，通过在驾驶前提供口头指示，系统地操纵了驾驶员对DMS的外显知识。比较了三种实验条件：驾驶员对DMS有明确的认识，驾驶员对DMS不了解，驾驶员对DMS不了解的对照组（介于因素之间）。比较第一次和重复与次要任务交互的目光行为，以评估视觉分散（内因素）。显性知识显著减少了超过2秒的扫视次数，表明视觉分心减少了。即使在与DMS反复相互作用后，这种效果仍然普遍存在。重要的是，在没有指示的情况下，仅仅激活DMS并不影响浏览行为。研究结果强调了显性知识在系统有效性中的重要作用。本研究通过对DMS驱动者心理模型的研究，得出了方法和实践意义，为DMS研究领域做出了贡献。

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