Searched for: author%3A%22de+Winter%2C+J.C.F.%22
(1 - 8 of 8)
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Lu, Z. (author), Happee, R. (author), de Winter, J.C.F. (author)
In highly automated driving, drivers occasionally need to take over control of the car due to limitations of the automated driving system. Research has shown that visually distracted drivers need about 7 s to regain situation awareness (SA). However, it is unknown whether the presence of a hazard affects SA. In the present experiment, 32...
journal article 2020
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Zhang, Bo (author), Lu, Z. (author), Happee, R. (author), de Winter, J.C.F. (author), Martens, Marieke (author)
In the context of automated driving, a monitoring request (MR) is a means to prepare drivers for a take-over event. However, driver compliance may be an issue because not all MRs require a take-over. In this study, we investigated how drivers’ compliance with MRs was associated with previously experienced scenarios. The compliance level was...
conference paper 2019
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Lu, Z. (author), Zhang, B. (author), Feldhütter, A. (author), Happee, R. (author), Martens, M. (author), de Winter, J.C.F. (author)
In conditionally automated driving, drivers do not have to monitor the road, whereas in partially automated driving, drivers have to monitor the road permanently. We evaluated a dynamic allocation of monitoring tasks to human and automation by providing a monitoring request (MR) before a possible take-over request (TOR), with the aim to...
journal article 2019
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Cabrall, C.D.D. (author), Lu, Z. (author), Kyriakidis, M. (author), Manca, L. (author), Dijksterhuis, C. (author), Happee, R. (author), de Winter, J.C.F. (author)
A common challenge with processing naturalistic driving data is that humans may need to categorize great volumes of recorded visual information. By means of the online platform CrowdFlower, we investigated the potential of crowdsourcing to categorize driving scene features (i.e., presence of other road users, straight road segments, etc.) at...
journal article 2018
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Lu, Z. (author), Coster, Xander (author), de Winter, J.C.F. (author)
Drivers of automated cars may occasionally need to take back manual control after a period of inattentiveness. At present, it is unknown how long it takes to build up situation awareness of a traffic situation. In this study, 34 participants were presented with animated video clips of traffic situations on a three-lane road, from an...
journal article 2017
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Lu, Z. (author), Happee, R. (author), de Winter, J.C.F. (author)
This study presents a numerical model that describes the dynamic process of building situation awareness after an automation-initiated transition. The model predicts the level of situation awareness as a function of elapsed time since the transition, and is verified using data from an experiment in which participants watched animated video...
conference paper 2017
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Lu, Z. (author), Happee, R. (author), Cabrall, C.D.D. (author), Kyriakidis, M. (author), de Winter, J.C.F. (author)
The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control,...
journal article 2016
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Lu, Z. (author), de Winter, J.C.F. (author)
The paper reviews some of the essentials of human-machine interaction in automated driving, focusing on control authority transitions. We introduce a driving state model describing the human monitoring level and the allocation of lateral and longitudinal control tasks. An authority transition in automated driving is defined as the process of...
conference paper 2015
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