Context: User assistance is generally defined as the guided assistance to a user of a software system in order to help accomplish tasks and enhance user experience. Automated user assistance systems are equipped with online help system that provides information to the user in an electronic format and which can be opened directly in the application. Various different automated user assistance approaches have been proposed in the literature. However, there has been no attempt to systematically review and report the impact of automated user assistance systems. Objective: The overall objective of this systematic review is to identify the state of art in automated user assistance systems, and describe the reported evidence for automated user assistance. Method: A systematic literature review is conducted by a multiphase study selection process using the published literature since 2002. Results: We reviewed 575 papers that are discovered using a well-planned review protocol, and 31 of them were assessed as primary studies related to our research questions. Conclusions: Our study shows that user assistance systems can provide important benefits for the user but still more research is required in this domain.
The Classification of Short and Long-term Driving Behavior for an Advanced Driver Assistance System by Analyzing Bidirectional Driving Features
洞察个人驾驶行为和习惯对于交通运营、安全和能源管理至关重要。随着互联车辆（CV）技术旨在解决所有这三个问题，驾驶模式的识别是为CV设计个性化高级驾驶员辅助系统（ADAS）的必要组成部分。我们的研究旨在通过采用一种独特的方法来分析驾驶员的双向（即纵向和横向）控制特征，使用一个简单的基于规则的分类过程来对他们的驾驶行为和习惯进行分组，从而满足这一需求。我们分析了来自密歇根州安娜堡的真实CV测试平台Safety Pilot Model Deployment的高分辨率驾驶数据，以识别高速公路，主干道和坡道道路类型上的各种驾驶行为。使用三个车辆特征，称为加加速度，领先车头时距，和横摆率，驾驶特性分为两组（安全驾驶和敌意驾驶）的短期分类，和驾驶员的习惯分为三类（冷静驾驶员，理性驾驶员，和侵略性驾驶员）。建议的分类模型进行了测试，未分类的数据集，以验证模型的信念，超速和陡峭的加速度。通过所提出的方法，行为分类已经成功地识别出了约90%的超速和类似水平的急性加速度实例。此外，我们的研究还提出了一个ADAS接口，该接口与驾驶员进行实时交互，以便将有关驾驶行为和习惯的信息转化为对单个驾驶员的反馈。我们提出了一种自适应和灵活的分类方法来识别短期和长期的驾驶行为，从自然驾驶数据识别，并最终传达不利的驾驶行为模式。
Analyzing Reluctance to Ask for Help When Cooperating With Robots: Insights to Integrate Artificial Agents in HRC
Interactive Robotics Group, Massachusetts Institute of Technology（麻省理工学院交互机器人组）；Department of Aeronautics and Astronautics（航空与航天系）；Department of Autonomous and Intelligent Systems（自主与智能系统系）；Faculty of Informatics, UPV/EHU（信息学院）
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
NYU Langone Health（纽约大学朗格尼健康中心）；Jawaharlal Institute of Postgraduate Medical Education and Research（贾瓦哈拉尔研究生医学教育与研究学院）；University of Toronto（多伦多大学）；Saint Louis University School of Medicine（圣路易斯大学医学院）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；Harvard Medical School（哈佛医学院）
Introducing ROADS: A Systematic Comparison of Remote Control Interaction Concepts for Automated Vehicles at Road Works
Ulm University（乌尔姆大学）；UCL Interaction Centre（伦敦大学学院交互中心）；RISE Research Institutes of Sweden（瑞典RISE研究院）；University of Salzburg（萨尔茨堡大学）；AIT Austrian Institute of Technology（奥地利技术研究院）
