What happens when drivers of automated vehicles take over control in critical brake situations?

Roche, Fabienne; Thüring, Manfred; Trukenbrod, Anna K.

doi:10.1016/j.aap.2020.105588

Cited by 28 publications

(8 citation statements)

References 19 publications

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“…Zhang et al (2019) endorsed this fact, and additionally noticed that reactions were faster and more appropriate in very risky situations, if drivers had experienced previous similar situations and if they were not performing another visual task during the automated driving. Zeeb et al (2016) confirmed the negative role of distraction, whereas Roche et al (2020) found a link between drivers' stress level and takeover overreactions. To what extend these tendencies apply to platooning scenarios and their consequences is still a research niche.…”

Section: Platooning Effects On Safety and Human Behaviormentioning

confidence: 99%

Platooning of connected automated vehicles on freeways: A bird’s eye view

Martínez-Díaz¹

2021

Datasets

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A platoon of connected automated vehicles (CAVs) is defined as a group of CAVs that exchange information, so that they can drive in a coordinated way, allowing very small spacings and, still, travelling safely at relatively high speeds. The concept of vehicle platooning is not new. Scientific articles on platooning have been published since the 1970s, and the first large-scale pilot test on vehicle platooning was carried out in the mid 1990s in California. By 1992, the first vehicle platooning experiments were successfully concluded, and the four-vehicle platoon capability was demonstrated for visitors on the I-15 HOV lanes in San Diego in 1994. The main purpose of these early research works was to improve traffic efficiency and reduce vehicle consumption, as well as to develop the existing technology, which represented a strong limitation at the time. Precisely, the development of new technologies and communications in the last decade has given a new impetus to the research on vehicle platooning on freeways, as one of the most promising forms of cooperation among CAVs. These recent studies have extended the analysis beyond traffic efficiency, including safety, sustainability, business productivity, among other objectives.In this context, today, there are many scientific publications on vehicle platooning with different purposes, scopes, scenarios, and based on a wide diversity of vehicles and technologies (i.e. regular or segregated lanes, cars or trucks, vehicles with different SAE levels, etc.). In order to organize and consolidate the existing knowledge on the field, a comprehensive and systematic review must be performed. The present work represents a first approach to this ambitious objective. First, platooning is conceptualized in order to facilitate its analysis and comparison among studies. Second, key publications on platooning are analyzed to determine the most significant impacts that can be expected from its implementation. Finally, some important research gaps and disparate findings on the topic are identified.

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Section: Platooning Effects On Safety and Human Behaviormentioning

confidence: 99%

Platooning of connected automated vehicles on freeways: A bird’s eye view

Martínez-Díaz¹

2021

Datasets

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“…Increased workload is associated with shorter TOT. Furthermore in [11], a short TOR time budget results in shorter TOT and poorer performances such as risk of somersault, accident, and rear collisions. Likewise [12], [13] concluded that elderly drivers react slower and therefore require more TOR time budget compared to younger drivers.…”

Section: B Takeover Time Performance and Tor Time Budgetmentioning

confidence: 99%

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Tanshi

Söffker

2022

IEEE Open J. Intell. Transp. Syst.

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An automated driving system notifies a fallback-ready human driver to resume driving when critical operational and functional limits have been or are about to be exceeded. The point between notification and critical limit is the time budget. Previous studies indicate that interdependencies exist between takeover variables and the time budget leading to performance variations and sometimes accidents. It is known that drivers may delay or respond inadequately depending on the time budget. This contribution focuses on utilizing these interdependencies to evaluate the suitability of the time budget for specific scenarios. A 7 s time budget, eight scenarios, and three secondary tasks were studied in a driving simulator with 70 participants aged between 19 Yrs and 41 Yrs. The results indicate that drivers prioritize takeover effort in decreasing order of relative speed, traffic agents, and junctions. Furthermore, 7 s is suitable at a vehicle speed of 80 Km/h to 130 km/h, maximum two traffic agents and three junctions, and handsfree tasks but too high for lower complexities. Generally, the time budget is a sum of the takeover time and maneuver (e.g., lane change) response time. These results are relevant to safety and adaptive variation of the time budget for successful takeover.INDEX TERMS Conditional driving automation, driver assistance, driver behavior, safety and human factors, takeover time.

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“…Although there are many measures of takeover efficiency, takeover response time is the most commonly used. Among these, researchers tend to gauge whether a driver is ready to takeover using steering or breaking thresholds: when the driver receives the request to takeover and prepares to operate the vehicle again, the threshold for determining the start of the driver takeover manoeuvre is 2 °steering wheel angle change and 10% free stroke of brake pedal [10,70,[73][74][75][76]. Take-over response time is measured against this because no change in vehicle operating status occurs when the control range is below these thresholds [76].…”

Section: Transitionmentioning

confidence: 99%

Effects of Level 3 Automated Vehicle Drivers’ Fatigue on Their Take-Over Behaviour: A Literature Review

Guo

Zhang

Cai

et al. 2021

Journal of Advanced Transportation

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With advancements in automated driving technology, Level 3 vehicle automation has been proliferating. In Level 3, drivers can turn their attention to things unrelated to driving and only takeover in cases of emergency. However, the transition to the main driving task can be affected by different physical and psychological changes in drivers such as fatigue and distraction. Although existing research in the field of manual driving has demonstrated the effects of fatigue on driver performance, there have been relatively few studies on fatigue in the field of automated driving. Fatigue decreases drivers’ alertness, which can prevent drivers from conducting necessary emergency takeover manoeuvres in a safe and timely manner. This systematic literature review was conducted to establish relationships between driving fatigue and takeover behaviour in the existing literature on Level 3 automated driving systems. Different from existing reviews of driving fatigue, this paper focuses on the commonly used and most effective evaluation indicators of driving fatigue and further discusses the methods to improve the takeover efficiency of fatigued drivers. This is the first detailed systematic review of general relationships between fatigue in automated driving and drivers’ takeover behaviours when the vehicle sends a takeover request. Researchers are invited to expand on the findings here to further clarify the findings on the associations between driver fatigue indicators in automated vehicle driving and drivers’ takeover response times.

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What happens when drivers of automated vehicles take over control in critical brake situations?

Cited by 28 publications

References 19 publications

Platooning of connected automated vehicles on freeways: A bird’s eye view

Platooning of connected automated vehicles on freeways: A bird’s eye view

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Effects of Level 3 Automated Vehicle Drivers’ Fatigue on Their Take-Over Behaviour: A Literature Review

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