Bio-inspired intent communication for automated vehicles

Various external human-machine interfaces (eHMIs) have been proposed that communicate the intent of automated vehicles (AVs) to vulnerable road users. However, there is no consensus on which eHMI concept is most suitable for intent communication. In nature, animals have evolved the ability to communicate intent via visual signals. Inspired by intent communication in nature, this paper investigated three novel and potentially intuitive eHMI designs that rely on posture, gesture, and colouration, respectively. In an online crowdsourcing study, 1141 participants viewed videos featuring a yielding or non-yielding AV with one of the three bio-inspired eHMIs, as well as a green/red lightbar eHMI, a walk/don’t walk text-based eHMI, and a baseline condition (i.e., no eHMI). Participants were asked to press and hold a key when they felt safe to cross and to answer rating questions. Together, these measures were used to determine the intuitiveness of the tested eHMIs. Results showed that the lightbar eHMI and text-based eHMI were more intuitive than the three bio-inspired eHMIs, which, in turn, were more intuitive than the baseline condition. An exception was the bio-inspired colouration eHMI, which produced a performance score that was equivalent to the text-based eHMI when communicating ‘non-yielding’. Further research is necessary to examine whether these observations hold in more complex traffic situations. Additionally, we recommend combining features from different eHMIs, such as the full-body communication of the bio-inspired colouration eHMI with the colours of the lightbar eHMI.     

Team situation awareness within the context of human-autonomy teaming

Effective team communication, a fundamental part of team coordination, is crucial for both effective Team Situation Awareness (TSA) and team performance. In this study, we looked at the role that team interaction (i.e., more specifically team verbal behaviors) played in TSA and team performance in order to better understand Human-Autonomy Teaming (HAT). We first analyzed team verbal behaviors (i.e., pushing and pulling information) across conditions of human-autonomy teams and human-human teams, and then analyzed their relationship with TSA and team performance via Growth Curve Modelling (GCM). Good teamwork involves anticipating the needs of teammates and that means pushing information before it is requested. Therefore, if things are going well, there should be little need for pulling information. In this study’s task, participants were instructed to push information to others, and over time master the specific timing of information sharing to the intended recipient. Findings indicate that pushing information was positively associated with TSA and team performance, and human-autonomy teams had lower levels of both pushing and pulling information than all-human teams. Through this study, we have learned that anticipation of other team member behaviors and information requirements in human-autonomy teams are important for effective TSA and team performance. In order to make HAT more effective in terms of teamwork, we need to develop mechanisms to enhance pushing information within HAT.     

小规模群体开放式与封闭式讨论任务的工作特性比较

１４个小组的大学生共６８人参加两种不同的群体工作,其中７组进行开放式问题的讨论,７组进行封闭式问题的讨论。从工作进程、工作模式和工作特点等方面记录群体工作行为：工作进程各阶段所用时间,发言的针对性,发言的连续性,说明问题的方式和深度,群体的意见分歧与支持行为和表达方式。结果表明：封闭式问题需要更多的时间澄清问题并且更多引用例子来阐述观点;讨论开放式问题多采用归纳推理而封闭式问题多采用演绎推理;无论在哪种问题的讨论中,针对个体的发言都显著多于针对整体的发言,而且人们更多地以非言语的方式而不是口头方式表示赞成。本文对以上结果在电视、电话和计算机辅助会议系统设计中的应用作了进一步讨论     

智能时代的工程心理学

智能技术为智能时代的工程心理学研究和应用提供了新的机遇。为此, 系统提出了智能时代工程心理学的工作框架。该工作框架包括工程心理学研究和应用的对象、核心问题空间、学科理念、研究重点、应用范围、方法等。智能时代的人机关系呈现出一种新的形式: 人机组队式的人机合作关系。“以人为中心的人工智能”应该是智能时代工程心理学的学科理念。针对智能技术, 近期工程心理学研究者开始开展围绕新型人机关系的理论框架和基本问题、人机组队中的心理结构和决策控制、人机交互等方面的研究工作。为有效支持智能系统的研发, 概括总结了一些工程心理学新方法和提升的方法。最后, 针对当前工程心理学所面对的一些挑战提出了具体的建议。     

Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments

External human machine interfaces (eHMI) might contribute to an enhanced traffic flow and road safety by providing relevant information to surrounding road users. To quantify the effect of eHMI on traffic flow, the majority of studies required participants to indicate their crossing decision in an explicit manner, such as pressing a button. While this approach proved to be efficient, the transfer to real-world behavior is unclear. Here, we propose a more realistic, motion-based approach allowing pedestrians to actually cross the road in front of a vehicle in a virtual reality environment. Participants (N = 51) encountered simulated automated vehicles (AVs) in two scenarios. We investigated the effect of different eHMIs on traffic flow and road safety. Pedestrians’́ body movements were obtained using a motion capturing system with six sensors. Our approach was validated using a two-step procedure. First, we assessed crossing behavior and subjective safety feeling while approaching AVs with and without eHMI. Second, we tested to which extent objective crossing behavior matched self-reported safety feeling. For this purpose, we evaluated if subjective safety feeling can be reliably predicted from actual crossing behavior using a functional data analysis. The proposed motion-based approach proved a valid investigation method for eHMI designs. The results indicated that eHMIs have a beneficial effect on traffic flow and road safety. Regarding traffic flow, participants crossed the road earlier and felt significantly safer when encountering an AV with an eHMI compared to no eHMI. In addition, in situations in which only some of the AVs were equipped with an eHMI, participants’ crossing behavior and safety feeling became more conservative for encounters without eHMI, indicating higher road safety. Further, subjective safety feeling was significantly predicted from actual crossing behavior. These findings highlight that eHMIs are beneficial for pedestrians’ crossing decision, both from an objective and subjective perspective.     

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

Communicating intent to develop shared situation awareness and engender trust in human-agent teams

This paper addresses issues related to integrating autonomy-enabled, intelligent agents into collaborative, human-machine teams. Interaction with intelligent machine agents capable of making independent, goal-directed decisions in human-machine teaming operations constitutes a major change from traditional human-machine interaction involving teleoperation. Communicating the machine agent’s intent to human counterparts becomes increasingly important as independent machine decisions become subject to human trust and mental models. The authors present findings from their research that suggest existing user display technologies, tailored with context-specific information and the human’s knowledge level of the machine agent’s decision process, can mitigate misperceptions of the appropriateness of agent behavioral responses. This is important because misperceptions on the part of human team members increases the likelihood of trust degradation and unnecessary interventions, ultimately leading to disuse of the agent. Examples of possible issues associated with communicating agent intent, as well as potential implications for trust calibration are provided.     

Best Practices for Teaming and Collaboration in the Interconnected Systems Framework

The Interconnected Systems Framework (ISF) blends school mental health practices, systems, and resources into all levels of a multitiered system of supports (e.g., positive behavior interventions and supports). The ISF aims to improve mental health and school performance for all students by emphasizing effective school-wide promotion and prevention; early identification; and social-emotional, behavioral, and mental health intervention. Teaming is 1 of 3 foundational components of quality ISF implementation at school, district, and state levels. After introducing the ISF, the authors highlight the core implementation components (i.e., teaming, data-based decision making, and evidence-based practices) and then elaborate on characteristics of well-functioning ISF teams. The authors provide a case example to illustrate each characteristic and review 5 measures teams may consider using to evaluate their functioning and SF implementation.     

人机交互中认知负荷变化预测模型的构建

设计模拟的人机交互实验, 分析持续作业过程中认知负荷在评估指标上的变化; 采用Elman神经网络和BP神经网络二种建模方法, 探索人机交互过程中认知负荷变化预测建模的构建方法。结果显示：持续作业中认知负荷在主任务反应时、主任务正确率、注视时间、注视次数4个评估指标上变化显著; 在心理努力、任务主观难度2个评估指标上变化不显著; Elman神经网络和BP神经网络两种预测模型可以对不同作业时间段认知负荷在评估指标上发生的变化进行预测; 再结合认知负荷的综合评估模型, 可实现对不同作业时间段个体认知负荷水平等级进行分析。     

An experimental investigation of the interaction between displays and models in multicriteria decision support

An experiment in multicriteria decision support was conducted that examined two types of information displays, two different multiattribute models and two levels of task complexity in a laboratory setting with students as subjects. The findings focused on the ‘process’ behaviour and the ‘design’ elements of the human-machine interface. Findings concerning the process behaviour suggested that the subjects' actions on data acquisition were guided by their cognitive preference for the attributes, but that the subjects had no discernible search pattern for evaluation. Findings for design included a significant interaction between display and model in weight estimation and assessment. The practical implications for software design are also discussed.