Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations

Gold, Christian; Körber, Moritz; Lechner, David; Bengler, Klaus

doi:10.1177/0018720816634226

Cited by 339 publications

(234 citation statements)

References 22 publications

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“…Our results showed that this process took less than 2 seconds, no matter in which emotional state drivers were. Recent studies comparing different types of NDRTs on takeover quality and timeliness showed that the types of NDRTs only influenced the takeover quality and not takeover time (Bueno et al, 2016;Gold et al, 2016;Körber et al, 2016;Zeeb et al, 2016;Zeeb, Härtel, Buchner, & Schrauf, 2017), providing further support for our findings.…”

Section: Discussionsupporting

confidence: 85%

“…In response to this known difficulty, research has been conducted to investigate factors affecting takeover performance, including the external driving environment (e.g., road elements, traffic situations, and weather conditions), types of NDRTs (e.g. reading, typing), individual characteristics (e.g., training, prior experience with automation, trust in automation, age), and design of human-machine interface (e.g., multi-modal display) (Eriksson & Stanton, 2017;Gold et al, 2016;Helldin, Falkman, Riveiro, & Davidsson, 2013;Wan & Wu, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers' takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers' takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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“…Previous research focused on the take-over situation, in which drivers have to take over the DDT after a period of automated driving. Results revealed an influence of traffic density (Radlmayr, Gold, Lorenz, Farid, & Bengler, 2014), (Gold, Körber, Lechner, & Bengler, 2016) or automation duration (Feldhütter, Gold, Schneider, & Bengler, 2017) on take-over performance. In addition, individual differences (Körber & Bengler, 2014) and NDRTs were analyzed (Merat, Jamson, Lai, & Carsten, 2012).…”

Section: Introductionmentioning

confidence: 97%

Peripheral Monitoring of Traffic in Conditionally Automated Driving

Radlmayr

Brüch

Schmidt

et al. 2018

Proceedings of the Human Factors and Ergonomics Society Annual

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Conditionally automated vehicles (level 3) allow drivers to engage in visual, non-driving related tasks (NDRTs) while the automation is active. System limits require drivers to reengage in the dynamic driving task in take-over situations. If the NDRT is visually engaging, situation awareness (SA) necessary for a successful take-over can decrease. This study analyzed, if the SA of drivers increases while monitoring the surrounding traffic peripherally. A semi-transparent balloon game in the head-up display operationalized the engagement into a visual NDRT with the possibility of peripheral monitoring. In addition, participants without the possibility of monitoring due to simulated heavy fog (second group) were tested along with a third group that could monitor surroundings self-determined without a NDRT. The between-subject design included 57 participants. Results showed that self-determined monitoring leads to higher situation awareness compared to peripheral monitoring and no monitoring. This did not result in better take-over performances.

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“…In an autonomous system, however, the user is not involved directly with the control of the vehicle, which presents questions of whether sobriety and attentiveness are required by the users. In fact, it is just as likely that users of HAVs might want to totally disengage from the driving process and instead focus on some other activity (e.g., answering email, working, sleeping, reading, watching a film, or engaging in some other forms of entertainment) (Gold & Bengler, ). These things may seem out of reach at present but, if HAVs operate as promised, there is no transportation safety‐centric reason to believe that individuals should not be engaged in any of these activities.…”

Section: The Case Of Autonomous Vehiclesmentioning

confidence: 99%

Regulating Autonomy: An Assessment of Policy Language for Highly Automated Vehicles

Schuelke‐Leech

Jordan

Barry

2019

Review of Policy Research

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Self-driving cars (also known as driverless cars, autonomous vehicles, and highly automated vehicles [HAVs]) will change the regulatory, political, and ethical frameworks surrounding motor vehicles. At the highest levels of automation, HAVs are operated by independent machine agents, making decisions without the direct intervention of humans. The current transportation system assumes human intervention though, including legal and moral responsibilities of human operators. Has the development of these artificial intelligence (AI) and autonomous system (AS) technologies outpaced the ethical and political conversations? This paper examines discussions of HAVs, driver responsibility, and technology failure to highlight the differences between how the policy-making institutions in the United States (Congress and the Public Administration) and technology and transportation experts are or are not speaking about responsibility in the context of autonomous systems technologies. We report findings from a big data analysis of corpus-level documents to find that enthusiasm for HAVs has outpaced other discussions of the technology.KEY WORDS: highly automated vehicles, autonomous vehicles, driverless cars, responsibility, policy making, big data analysis 监管自治：评估关于高度自动化汽车的政策语言自动驾驶汽车(也被称为无人驾驶汽车、自动汽车和高度自动化汽车(HAVS) )将对有关汽车的监管框架、政治框架和道德框架产生显著作用。处于最高级别的自动化，这些汽车的运行由独立机器部件完成，后者能在没有人类直接监管或干预的情况下作出决定。当前运输系统的预测基于人类操纵者的法律责任和道德责任。HAVS则对该预测发起了挑战。运输系统正由以人为中心的系统转向由人类和机器共同合作的系统。这些人工智能(AI)和自主系统(AS)的发展已超越了关于这些技术的道德交流和政治交流了吗？是否存在一个用于应对这些独立的，非人类智能体的过程和框架？本文审视了围绕发展中汽车技术、司机责任和技术失败的相关探讨，以探索并对比政策制定者和技术专家之间的差异。笔者描述了美国主要政策制定机构--即国会和公共行政--如何谈论/或不谈论自主系统技术。笔者聚焦于每个机构在探讨HAVS背景下相关责任时所使用的语言。针对一项关于四个政策机构语料库文档的大数据评论，笔者得出研究发现：对技术所持的热情已超越了有关技术的政策讨论和道德讨论。关键词: 高科技, 创新, 交通运输, 治理 Regulación de la autonomía: una evaluación del lenguaje de políticas para vehículos altamente automatizadosLos automóviles autónomos (también conocidos como automóviles sin conductor, vehículos autónomos y vehículos altamente automatizados (HAV)) tendrán un efecto significativo en los marcos normativos, políticos y éticos que rodean a los vehículos de motor. En los niveles más altos de automatización, estos vehículos son operados por agentes de máquinas independientes, capaces de tomar decisiones sin la supervisión directa o las intervenciones de los seres humanos. El sistema de transporte actual se

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Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations

Abstract: The present results can be used by developers of highly automated systems to appropriately design human-machine interfaces and to assess the driver's time budget for regaining control.

Cited by 339 publications

References 22 publications

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Peripheral Monitoring of Traffic in Conditionally Automated Driving

Regulating Autonomy: An Assessment of Policy Language for Highly Automated Vehicles

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