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Comfort in Automated Driving: A Literature Survey and a High-Level Integrative Framework

背景(考古学) 分类 膨胀的 自动化 人机交互 资源(消歧) 组分(热力学) 集合(抽象数据类型) 计算机科学 控制(管理) 工程类 人工智能 古生物学 程序设计语言 材料科学 复合材料 抗压强度 物理 热力学 生物 机械工程 计算机网络
作者
Veronika Domova,Rebecca Currano,David Sirkin
出处
期刊:Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies [Association for Computing Machinery]
卷期号:8 (3): 1-23 被引量:2
标识
DOI:10.1145/3678583
摘要

The advancement of automated vehicle technology and the resulting shift from active driver control to a more passive role introduce previously unexplored factors that influence drivers' comfort. Examples include the vehicle's level of automation and the driver's preferred driving style, trust in the vehicle, and situation awareness. To structure these as a resource that can support future research, we conducted a comprehensive literature review, identifying 51 works that directly or peripherally address comfort in an automated driving context. Most of these works focus on the physical component of comfort, rooted in vehicle dynamics, while only a few consider a broader concept of comfort necessary to encompass a more expansive set of factors. Based on this review, we propose an integrative framework of 27 comfort influencing factors and their interrelationships. We categorize factors into six groups, encompassing the driving environment, vehicle physical features and automation system, and the user's activity, individual characteristics, and understanding of the automated system. These six groups are organized into the three larger categories of environment, vehicle, and user-related considerations. Patterns that emerge from the framework include that: a) some factors primarily influence physical well-being (such as motion forces), b) some contribute to discomfort (automation failures) while others contribute to comfort (secondary activities), c) some are stable and known before the trip (individual characteristics) while others change over time (environment), and d) comfort or discomfort can lead users to change either the relevant factors (level of automation) or their own behavior (secondary activities).
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