L3等级自动驾驶条件下驾驶员接管过程及心理模型的构建

自动驾驶是当今智能交通的研究热点,目前正处于由L2等级向L3等级过渡的时期。接管过程作为L3等级自动驾驶车辆人机交互的核心概念,引起了大量研究人员的注意。文章在已有研究的基础上将接管过程划分为警觉唤醒、接管决策和接管执行三个阶段,结合人口学、非驾驶相关任务、驾驶疲劳、信任态度等影响因素加以完善,进一步提出接管过程的三阶段模型。并对接管过程影响因素的未来研究方向,心理机制的构建以及应用现状提出建议。     

基于认知机制的有条件自动驾驶接管中的人因问题

自动驾驶能在很大程度上缓解现代交通问题并提升驾驶舒适度。有条件自动驾驶下, 驾驶员可执行非驾驶相关任务但需要在系统无法处理的状况下接管车辆。在这一关键过程中, 驾驶员需要进行注意转换并获得情境意识以成功接管。已有研究表明, 接管请求、非驾驶相关任务、驾驶情景及驾驶员因素是影响接管过程重要因素。未来可从认知机制角度研究各因素对接管过程产生的影响, 以及探究接管过程中各因素之间可能存在的交互作用。     

不同驾驶条件对于驾驶员情景意识的影响

本文详述了两种驾驶条件对于驾驶员情景意识的影响。手动驾驶是目前最为常用的驾驶方式,驾驶员需要手脚配合来完成驾驶任务,并且要时刻关注于道路情况。高度自动化驾驶系统(HAD)是基可以同时控制机动车的纵向运动和横向运动。根据之前的研究,手动驾驶带有HAD辅助系统的驾驶的情况下,可以将更多的注意资源分配在与驾驶无关的其他任务上。驾驶员驾驶带有HAD辅助系统的车辆与手动驾驶相比,驾驶员识别环境中客体的能力有所提升。但是在驾驶员驾驶过程当中同时执行与驾驶无关的任务,驾驶带有HAD系统与手动驾驶相比,情景意识较差。     

驾驶适性理论若干问题探讨

现代化建设的飞速发展,作为经济命脉的交通运输愈来愈显出其重要作用。要使交通运输畅通无阻、安全有序,驾驶员的素质是个关键。众所周知,人(驾驶员和行人)、车辆与道路是构成交通的三大要素,而人(特别是驾驶员)是主导的核心的因素。有些人适宜于驾驶作业,有些     

影响驾驶安全的驾驶员注意模式研究述评

驾驶员的视觉注意模式对于汽车安全驾驶具有非常重要的意义。本文主要从驾驶员的路面视觉扫描模式和整体视觉场景的注意模式两个方面对国内外有关研究进行了概览。主要包括: (1)视觉扫描模式对驾驶安全的影响研究; (2)驾驶员视觉注意分配模型研究; (3)以及针对驾驶员视觉扫描模式的培训研究。并提出进一步研究有待于深入探讨影响驾驶员视觉注意模式的影响因素以及开展适合我国汽车驾驶特点的本土化研究。     

依赖结构对信任行为的影响:基于互依理论的视角

信任是人际互动中的重要主题,受到诸多因素的影响,越来越多的研究者关注情境特征对信任的影响。以相互依赖理论为基础,操作依赖的相互性、依赖水平和利益协同,构建互动双方间不同的依赖结构,分析不同依赖结构对被试信任的影响。结果表明:(1)在双向依赖情境中被试的信任水平显著高于单向依赖情境;(2)单向依赖、低利益协同情境中,依赖水平对个体的信任行为有显著影响,双向依赖、高利益协同情境中,依赖水平对个体信任行为的影响不显著;(3)单向依赖、低依赖水平情境中,利益协同对信任行为有显著影响,双向依赖、高利益协同情境中,利益协同对信任行为影响不显著。     

自动驾驶汽车与行人交互中的沟通界面设计：基于行人过街决策模型的评估

自动驾驶汽车要进入人车混行的普通道路, 需确保与过街行人之间的交互安全和效率。为解决这一问题, 高等级自动驾驶汽车往往在车辆外部装置显示设备, 即外部人机界面(eHMIs)以和行人沟通信息。在具体设计上, 已有研究主要采用文字、图形、投影等视觉沟通形式, 传达车辆状态(是否在自动驾驶模式)、意图和对行人的过街建议等沟通信息, 并在真实路段实验、虚拟场景及实验室实验等情境中评估了界面的使用对行人过街意向、速度和准确性等指标的影响。然而, 以行人为中心的外部界面设计需系统地支持行人过街决策前各阶段的信息加工需求。因此, 我们结合行人过街决策过程和情境意识理论, 提出行人与自动驾驶汽车交互中的动态过街决策模型, 从行人认知加工视角评估各种界面的沟通效果。评估的结果启示, eHMIs应促进行人对车辆信息的感知、理解和预测。在感知阶段, 应采用多种类型界面、多呈现载体相结合, 增强信息的可识别性。在理解阶段, 需结合文字说明、合理选择沟通视角、信号标准化和培训提高可理解性。在预测阶段, 应结合车辆内隐运动信息, 帮助行人快速准确获取车辆未来行动意图。更重要的是, 未来研究应关注在多行人、多车辆混行情境下的信息沟通设计及其对行人的影响。理论方面, 未来研究也需要关注外部界面如何通过自下而上的通路影响情境意识和心智模型的形成。     

组织内信任与创造力的关系：元分析的证据

采用元分析技术探讨了组织内部水平信任、垂直信任和系统信任对创造力的影响。通过文献搜索纳入元分析的研究有85项, 共99个独立效应量。其中, 水平信任与创造力关系的元分析有41个独立样本, 垂直信任与创造力关系的元分析有34个独立样本, 系统信任与创造力关系的元分析有24个独立样本。元分析结果表明, 水平信任(r = 0.50)、垂直信任(r = 0.38)和系统信任(r = 0.48)与创造力之间存在显著正相关。水平信任、垂直信任、系统信任三者与创造力的关系受到信任测量工具的调节作用, 但是不受文化背景和知识水平的调节影响。     

社会困境下政治信任对公众态度和合作行为的影响

现代社会面临的重要问题是如何有效地促进人们之间的相互合作,达到社会公共利益的最大化。因此,有关社会困境(social dilemma)的研究成为社会心理学领域的热点。随着群体理论的发展,研究者的研究视角逐渐从个体(individual)转向群体(collective),关注层级结构的群体(hierarchical groups)中,管理者或管理机构(权威)的特征和行为对个体合作行为的影响。其中,权威信任(trust in authority)和公正感(fairness)是影响个体态度和合作行为的重要变量。政治信任(political trust)也可以看作权威信任的一种,即在社会背景下,公众对社会管理权威(政府机构)的信任。未来研究应尝试在实验室里对政治信任的作用和机制进行探究,并进一步探究公正感在政治信任对态度及合作行为关系中的中介作用。     

团队信任的影响因素与作用机制:基于团队层面的分析

团队信任既包括团队成员之间个体层面的人际信任,也包括团队成员将团队作为一个整体所形成的团队层面的信任.团队层面影响团队信任的因素包括团队特征、团队运行过程、团队沟通与冲突,以及团队领导者特征等,并且团队信任直接或间接(通过一些中介和调节变量)对团队效能产生影响.未来的研究可进一步关注团队信任的测量问题、跨层次的借鉴与整...     

Impact of interface design on drivers’ behavior in partially automated cars: An on-road study

In partially automated vehicles, the driver and the automated system share control of the vehicle. Consequently, the driver may have to switch between driving and monitoring activities. This can critically impact the driver’s situational awareness. The human–machine interface (HMI) is responsible for efficient collaboration between driver and system. It must keep the driver informed about the status and capabilities of the automated system, so that he or she knows who or what is in charge of the driving. The present study was designed to compare the ability of two HMIs with different information displays to inform the driver about the system’s status and capabilities: a driving-centered HMI that displayed information in a multimodal way, with an exocentric representation of the road scene, and a vehicle-centered HMI that displayed information in a more traditional visual way. The impact of these HMIs on drivers was compared in an on-road study. Drivers’ eye movements and response times for questions asked while driving were measured. Their verbalizations during the test were also transcribed and coded. Results revealed shorter response times for questions on speed with the exocentric and multimodal HMI. The duration and number of fixations on the speedometer were also greater with the driving-centered HMI. The exocentric and multimodal HMI helped drivers understand the functioning of the system, but was more visually distracting than the traditional HMI. Both HMIs caused mode confusions. The use of a multimodal HMI can be beneficial and should be prioritized by designers. The use of auditory feedback to provide information about the level of automation needs to be explored in longitudinal studies.     

The utility of psychological measures in evaluating perceived usability of automated vehicle interfaces – A study with older adults

The design of the traditional vehicle human-machine interfaces (HMIs) is undergoing major change as we move towards fully connected and automated vehicles (CAVs). Given the diversity of user requirements and preferences, it is vital for designers to gain a deeper understanding of any underlying factors that could impact usability. The current study employs a range of carefully selected psychological measures to investigate the relationship with self-report usability of an in-CAV HMI integrated into a fully automated Level 5 simulator, during simulated journeys. Twenty-five older adults (65-years+) participated and were exposed to four journeys in a virtual reality fully automated CAV simulator (with video recorded journeys) into which our HMI was integrated. Participants completed a range of scales and questionnaires, as well as computerized cognitive tests. Key measures were: perceived usability of the HMI, cognitive performance, personality, attitudes towards computers, trust in technology, simulator sickness, presence and emotion. HMI perceived usability correlated positively with cognitive performance (e.g., working memory) and some individual characteristics such as trust in technology and negatively with neuroticism anxiety. Simulator sickness was associated negatively with CAV HMI perceived usability. Positive emotions correlated positively with reported usability across all four journeys, while negative emotions were negatively associated with usability only in the case of the last two journeys. Increased sense of presence in the virtual CAV simulator was not associated with usability. Implications for design are critically discussed. Our research is highly relevant in the design of high-fully automated vehicle HMIs, particularly for older adults, and in informing policy-makers and automated mobility providers of how to improve older people’s uptake of this technology.     

The impact of an in-vehicle display on glance distribution in partially automated driving in an on-road experiment

One of the major challenges of designing an HMI for partially automated vehicles is the trade-off between a sufficient level of system information and avoidance of distracting the driver. This study aimed to investigate drivers’ glance behavior as an indicator of distraction when vehicle guidance is partially automated. Therefore, an on-road experiment was conducted comparing two versions of an in-vehicle display (during partially automated driving) and no display (during manual driving) on a heavy congested highway segment. The distribution of drivers’ total glance durations on the HMI showed that visual attention was shifted away from monitoring the central road scene towards looking at the in-vehicle display to a considerable extent. However, an analysis of the distribution of single glance durations supports the view that using partial automation and a respective HMI does not lead to a critical increase in distraction. Driving with a simplified version of the HMI had the potential to reduce glance duration on and thus potential distraction of the in-vehicle display.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

Visualizing the autonomous vehicle’s maneuvers – Does an ecological interface help to increase the hedonic quality and safety?

This paper examines whether ecological speed information describing ongoing driving maneuvers during automated driving enhances the hedonic quality and driving safety immediately after a driving takeover. Visualizing maneuvers and trajectories has already proven effective. However, planned acceleration and deceleration in an automated vehicle have not yet been investigated. Therefore, this paper assesses how an automated vehicle’s speed control information might be presented by an ecological interface. Besides a possible increase in the hedonic quality, this information might enhance safe behavior of the human driver when it comes to a takeover. To assess these two aspects, 43 drivers participated in a dynamic driving simulator study. Using a within-subject design, two scenarios were used to compare an ecological interface, dynamically visualizing speed changes, to a conventional pop-up interface, using pop-up icons to visualize speed changes. The experimental results indicate that ecological feedback and conventional pop-up feedback do not differ regarding the hedonic quality, which was reflected by the state anxiety, usefulness, and satisfaction with the overall human-machine interface (HMI). Nonetheless, the post-hoc questionnaire on situational awareness showed a significantly lower rating for the ecological interface which may be the result of a more automatic and subconscious processing of the information given. Analyzing the takeover performance, the initial takeover time was comparably low for both interfaces. However, concerning safety, the ecological interface significantly enhanced the lateral control after takeover, and the drivers looked at the vehicle mirrors significantly earlier. In conclusion, the results show that the information given by the ecological interface may help drivers cope with a sudden takeover in a faster and more controlled way. Future applications of these findings might serve to enhance the acceptance and safety of semi-autonomous vehicles by implementing ecological interfaces.     

Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles

To prompt the use of driving automation in an appropriate and safe manner, system designers require knowledge about the dynamics of driver trust. To enhance this knowledge, this study manipulated prior information of a partial driving automation into two types (detailed and less) and investigated the effects of the information on the development of trust with respect to three trust attributions proposed by Muir (1994): predictability, dependability, and faith. Furthermore, a driving simulator generated two types of automation failures (limitation and malfunction), and at six instances during the study, 56 drivers completed questionnaires about their levels of trust in the automation. Statistical analysis found that trust ratings of automation steadily increased with the experience of simulation regardless of the drivers’ levels of knowledge. Automation failure led to a temporary decrease in trust ratings; however, the trust was rebuilt by a subsequent experience of flawless automation. Results showed that dependability was the most dominant belief of drivers’ trust throughout the whole experiment, regardless of their knowledge level. Interestingly, detailed analysis indicated that trust can be accounted by different attributions depending on the drivers’ circumstances: the subsequent experience of error-free automation after the exposure to automation failure led predictability to be a secondary predictive attribution of drivers’ trust in the detailed group whilst faith was consistently the secondary contributor to shaping trust in the less group throughout the experiment. These findings have implications for system design regarding transparency and for training methods and instruction aimed at improving driving safety in traffic environments with automated vehicles.     

Secondary task engagement and disengagement in the context of highly automated driving

During highly automated driving (level 3 automation according to SAE International, 2014) people are likely to increase the frequency of secondary task interactions. However, the driver must still be able to take over control within a reasonable amount of time. Previous studies mainly investigated take-over behavior by forcing participants to engage in secondary tasks prior to take over, and barely addressed how drivers voluntarily schedule secondary task processing according to the availability and predictability of automated driving modes. In the current simulator study 20 participants completed a test drive with alternating sections of manual and highly automated driving. One group had a preview on the availability of the automated driving system in upcoming sections of the track (predictive HMI), while the other drivers served as a control group. A texting task was offered during both driving modes and also prior to take-over situations. Participants were free to accept or reject a given task, taking the situational demands into account. Drivers accepted more tasks during highly automated driving. Furthermore, tasks were rejected more often prior to take-over situations in the predictive HMI group. This was accompanied by safer take-over performance. However, once engaged in a task, drivers tended to continue texting even in take-over situations. The results indicate the need to discriminate different aspects of task handling regarding self-regulation: task engagement and disengagement.     

Investigating explanations in conditional and highly automated driving: The effects of situation awareness and modality

With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not necessary to require the drivers to intervene on most occasions, it is still important to improve drivers’ situation awareness (SA) in unexpected driving scenarios to improve their trust in and acceptance of AVs. In this study, we conceptualized SA at the levels of perception (SA L1), comprehension (SA L2), and projection (SA L3), and proposed an SA level-based explanation framework based on explainable AI. Then, we examined the effects of these explanations and their modalities on drivers’ situational trust, cognitive workload, as well as explanation satisfaction. A three (SA levels: SA L1, SA L2 and SA L3) by two (explanation modalities: visual, visual + audio) between-subjects experiment was conducted with 340 participants recruited from Amazon Mechanical Turk. The results indicated that by designing the explanations using the proposed SA-based framework, participants could redirect their attention to the important objects in the traffic and understand their meaning for the AV system. This improved their SA and filled the gap of understanding the correspondence of AV’s behavior in the particular situations which also increased their situational trust in AV. The results showed that participants reported the highest trust with SA L2 explanations, although the mental workload was assessed higher in this level. The results also provided insights into the relationship between the amount of information in explanations and modalities, showing that participants were more satisfied with visual-only explanations in the SA L1 and SA L2 conditions and were more satisfied with visual and auditory explanations in the SA L3 condition. Finally, we found that the cognitive workload was also higher in SA L2, possibly because the participants were actively interpreting the results, consistent with a higher level of situational trust. These findings demonstrated that properly designed explanations, based on our proposed SA-based framework, had significant implications for explaining AV behavior in conditional and highly automated driving.     

Human machine interface design for continuous support of mode awareness during automated driving: An online simulation

In the transition towards higher levels of vehicle automation, one of the key concerns with regards to human factors is to avoid mode confusion, when drivers misinterpret the driving mode and therewith misjudge their own tasks and responsibility. To enhance mode awareness, a clear human centered Human Machine Interface (HMI) is essential. The HMI should support the driver tasks of both supervising the driving environment when needed and self-regulating their non-driving related activities (NDRAs). Such support may be provided by either presenting continuous information on automation reliability, from which the driver needs to infer what task is required, or by presenting continuous information on the currently required driving task and allowed NDRA directly. Additionally, it can be valuable to provide continuous information to support anticipation of upcoming changes in the automation mode and its associated reliability or required and allowed driver task(s). Information that could support anticipation includes the available time until a change in mode (i.e. time budget), information on the upcoming mode, and reasons for changing to the upcoming mode. The current work investigates the effects of communicating this potentially valuable information through HMI design. Participants received information from an HMI during simulated drives in a simulated car presented online (using Microsoft Teams) with an experimenter virtually accompanying and guiding each session. The HMI either communicated on automation reliability or on the driver task, and either included information supporting anticipation or did not include such information. Participants were thinking aloud during the simulated drives and reported on their experience and preferences afterwards. Anticipatory information supported understanding about upcoming changes without causing information overload or overreliance. Moreover, anticipatory information and information on automation reliability, and especially a combination of the two, best supported understandability and usability. Recommendations are provided for future work on facilitating supervision and NDRA self-regulation during automated driving through HMI design.     

Modeling the effects of perceived intuitiveness and urgency of various auditory warnings on driver takeover performance in automated vehicles

Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles.