Expert-based controllability assessment of control transitions from automated to manual driving

Naujoks, Frederik; Wiedemann, Katharina; Schömig, Nadja; Jarosch, Oliver; Gold, Christian

doi:10.1016/j.mex.2018.05.007

Cited by 38 publications

(19 citation statements)

References 12 publications

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“…For example, prior research has suggested that the number of gaze switches (i.e., monitoring behavior) can serve as an indicator for trust/reliance [18], or interface understanding [29]. One solution to the difficulty and extensive effort of collecting behavioral data might be experimenters' single-item ratings of interaction performance [139][140][141]. However, this approach requires well-trained raters and, ideally, ratings are given single-blind, so that the rater is not aware of assigned experimental conditions for the participant.…”

Section: Data Collection Methodsmentioning

confidence: 99%

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

et al. 2020

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During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.

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Section: Data Collection Methodsmentioning

confidence: 99%

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

et al. 2020

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“…Both complexities and the familiarity are assumed to influence the takeover quality. The quality of the takeover is rated using the take-over controllability rating (TOC; [18]). The TOC is a procedure for an assessment of control transitions from automated to manual driving.…”

Section: Takeover Qualitymentioning

confidence: 99%

“…Endangerment is rated between seven and nine, including endangerment of others and self-endangerment over all the sub-scales. In cases of non-controllable events, the takeover is rated with a ten, including collision, lane departure/leaving road or loss of vehicle control over all the sub-scales [18]. Low values indicate a faultless takeover (= 1) and high quality.…”

Section: Takeover Qualitymentioning

confidence: 99%

The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

2020

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In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing factors is the complexity of a traffic situation that has not been sufficiently addressed so far, as different approaches towards complexity exist. This paper differentiates between the objective complexity and the subjectively perceived complexity. In addition, the familiarity with a takeover situation is examined. Gold et al. show that repetition of takeover scenarios strongly influences the take-over performance. Yet, both complexity and familiarity have not been considered at the same time. Therefore, the aim of the present study is to examine the impact of objective complexity and familiarity on the subjectively perceived complexity and the resulting takeover quality. In a driving simulator study, participants are requested to take over vehicle control in an uncritical situation. Familiarity and objective complexity are varied by the number of surrounding vehicles and scenario repetitions. Subjective complexity is measured using the NASA-TLX; the takeover quality is gathered using the take-over controllability rating (TOC-Rating). The statistical evaluation results show that the parameters significantly influence the takeover quality. This is an important finding for the design of cognitive assistance systems for future highly automated and intelligent vehicles.

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“…Expert rating of takeover performance based on the video. For that the take-over controllability (TOC) rating was performed ( [27], for more details see https://toc-rating.de/en/) which defines a standardized procedure for the evaluation of take-over situations. Relevant dimensions for evaluation are provided with defined criteria for the different rating categories.…”

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confidence: 99%

Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage

et al. 2020

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Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.

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Expert-based controllability assessment of control transitions from automated to manual driving

Abstract: Graphical abstract

Cited by 38 publications

References 12 publications

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage

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