Towards the application of evidence accumulation models in the design of (semi-)autonomous driving systems – an attempt to overcome the sample size roadblock

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS
Dominik Bachmann , Leendert van Maanen
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引用次数: 0

Abstract

For the foreseeable future, automated vehicles (AVs) will coexist on the roads with human drivers. To avoid accidents, AVs will require knowledge on how human drivers typically make high-stakes and time-sensitive decisions (e.g., whether or not to brake). Providing such insights could be statistical models designed to explain human information processing and decision making. This paper attempts to address a roadblock that prevents one class of such "cognitive models", evidence accumulation models (EAMs), from being widely applied in the design of AV systems: their high demands for data. Specifically, we investigate whether Bayesian hierarchical modeling can be used to determine a person's characteristics, if we only have limited data about their behavior but extensive data on other (comparable) people's behaviors. Leveraging a simulation study and a reanalysis of experimental data, we find that most parameters of Decision Diffusion Models (a class of EAMs) – representing information processing components – can be adequately estimated with as few as 20 observations, if prior information regarding the decision-making processes of the population is incorporated. Subsequently, we discuss the implications of our findings for the modeling of traffic situations.

在(半)自动驾驶系统设计中应用证据积累模型--克服样本量障碍的尝试
在可预见的未来,自动驾驶汽车(AV)将与人类驾驶员共存于道路上。为了避免事故,自动驾驶汽车需要了解人类驾驶员通常是如何做出高风险、高时效决策的(例如是否刹车)。提供这种见解的可能是旨在解释人类信息处理和决策制定的统计模型。本文试图解决阻碍这类 "认知模型"--证据积累模型(EAM)--广泛应用于视听系统设计的一个障碍:它们对数据的高要求。具体来说,我们研究了如果我们只有有限的个人行为数据,却有大量其他(可比)人的行为数据,那么是否可以使用贝叶斯层次模型来确定一个人的特征。通过模拟研究和对实验数据的重新分析,我们发现,如果纳入有关人群决策过程的先验信息,决策扩散模型(EAMs 的一种)(代表信息处理组件)的大多数参数只需 20 个观测值就能得到充分估计。随后,我们将讨论我们的发现对交通状况建模的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies 工程技术-计算机:控制论
CiteScore
11.50
自引率
5.60%
发文量
108
审稿时长
3 months
期刊介绍: The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...
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