cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/19102133
title: Supplementary data for the paper 'Crowdsourced assessment of 227 text-based eHMIs for a crossing scenario'

description: Future automated vehicles may be equipped with external human-machine interfaces (eHMIs) capable of signaling whether pedestrians can cross the road. Industry and academia have proposed a variety of eHMIs featuring a text message. An eHMI message can refer to the action to be performed by the pedestrian (egocentric message) or the automated vehicle (allocentric message). Currently, there is no consensus on the correct phrasing of the text message. We created 227 eHMIs based on text-based eHMIs observed in the literature. A crowdsourcing experiment (<i>N</i> = 1241) was performed with images depicting an automated vehicle equipped with an eHMI on the front bumper. The participants indicated whether they would (not) cross the road, and response times were recorded. Egocentric messages were found to be more compelling for participants to (not) cross than allocentric messages. Furthermore, Spanish-speaking participants found Spanish eHMIs more compelling than English eHMIs. Finally, it was established that some eHMI texts should be avoided, as signified by compellingness, long responses, and high inter-subject variability.
DOI: 10.4121/13488384.v1
title: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.

description: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.
DOI: 10.4121/13488384.v2
title: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.

description: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.
DOI: 10.4121/13488384
title: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.

description: Supplementary data for the paper: How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study.
DOI: 10.4121/12948650.v1
title: Supplementary data for the paper: External human-machine interfaces for automated cars: What is the right color for signaling ‘Please cross the road?

description: Supplementary data for the paper Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2020). External human-machine interfaces for automated cars: What is the right color for signaling ‘Please cross the road?’
DOI: 10.4121/12948650.v3
title: Supplementary data for the paper 'External Human-Machine Interfaces: Which of 729 Colors Is Best for Signaling ‘Please (Do not) Cross’?'

description: Supplementary data for the paper Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2020). External Human-Machine Interfaces: Which of 729 colors is best for signaling ‘Please (do not) cross’? <em>IEEE International Conference on Systems, Man and Cybernetics (SMC). </em>Toronto, ON, Canada.
DOI: 10.4121/12948650
title: Supplementary data for the paper 'External Human-Machine Interfaces: Which of 729 Colors Is Best for Signaling ‘Please (Do not) Cross’?'

description: Supplementary data for the paper Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2020). External Human-Machine Interfaces: Which of 729 colors is best for signaling ‘Please (do not) cross’? <em>IEEE International Conference on Systems, Man and Cybernetics (SMC). </em>Toronto, ON, Canada.
DOI: 10.4121/13614824.v1
title: Supplementary data for the paper 'crowdsourced gazes'

description: In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians’ attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants’ eye-gaze data were collected using the TurkEyes method. The method involved briefly showing codecharts after each image and asking the participants to type the code they saw last. The results indicate that automated vehicles were more often glanced at than manual vehicles. Measuring eye gaze without an eye tracker is promising.
DOI: 10.4121/13614824.v2
title: Supplementary data for the paper 'crowdsourced gazes'

description: In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians’ attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants’ eye-gaze data were collected using the TurkEyes method. The method involved briefly showing codecharts after each image and asking the participants to type the code they saw last. The results indicate that automated vehicles were more often glanced at than manual vehicles. Measuring eye gaze without an eye tracker is promising.
DOI: 10.4121/13614824.v3
title: Supplementary data for the paper 'crowdsourced gazes'

description: In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians’ attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants’ eye-gaze data were collected using the TurkEyes method. The method involved briefly showing codecharts after each image and asking the participants to type the code they saw last. The results indicate that automated vehicles were more often glanced at than manual vehicles. Measuring eye gaze without an eye tracker is promising.
DOI: 10.25574/49744
title: Chandra X-ray Observatory ObsId 49744
DOI: 10.4121/13614824
title: Supplementary data for the paper 'crowdsourced gazes'

description: In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians’ attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants’ eye-gaze data were collected using the TurkEyes method. The method involved briefly showing codecharts after each image and asking the participants to type the code they saw last. The results indicate that automated vehicles were more often glanced at than manual vehicles. Measuring eye gaze without an eye tracker is promising.
DOI: 10.4121/uuid:9ea78136-3ffc-4194-84f7-3116b6a55758
title: Supplementary data for the article: Survey on eHMI concepts: The effect of text, color, and perspective

description: Supplementary data for the paper: Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F.
DOI: 10.4121/12708869.v2
title: Supplementary data for the article: Survey on eHMI concepts: The effect of text, color, and perspective

description: Supplementary data for the paper: Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F.
DOI: 10.4121/12708869
title: Supplementary data for the article: Survey on eHMI concepts: The effect of text, color, and perspective

description: Supplementary data for the paper: Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F.
DOI: 10.4121/uuid:e3908ec5-d086-4737-8d4a-d4046dbbc53c
title: Supplementary material for the paper: "Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays"

description: Raw data and scripts for the paper: Bazilinskyy, P., Petermeijer, S. M., Petrovych, V., Dodou, D., & De Winter, J. C. F. (2018). Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays. Transportation Research Part F
DOI: 10.4121/12717677.v2
title: Supplementary material for the paper: "Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays"

description: Raw data and scripts for the paper: Bazilinskyy, P., Petermeijer, S. M., Petrovych, V., Dodou, D., & De Winter, J. C. F. (2018). Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays. Transportation Research Part F
DOI: 10.4121/12717677
title: Supplementary material for the paper: "Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays"

description: Raw data and scripts for the paper: Bazilinskyy, P., Petermeijer, S. M., Petrovych, V., Dodou, D., & De Winter, J. C. F. (2018). Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays. Transportation Research Part F
DOI: 10.4121/14077352.v1
title: Supplementary data for the paper 'What driving style makes pedestrians think a passing vehicle is driving automatically?'

description: An important question in the development of automated vehicles (AVs) is which driving style AVs should adopt and how other road users perceive them. The current study aimed to determine which AV behaviours contribute to pedestrians’ judgements as to whether the vehicle is driving manually or automatically as well as judgements of likeability. We tested five target trajectories of an AV in curves: playback manual driving, two stereotypical automated driving conditions (road centre tendency, lane centre tendency), and two stereotypical manual driving conditions, which slowed down for curves and cut curves. In addition, four braking patterns for approaching a zebra crossing were tested: manual braking, stereotypical automated driving (fixed deceleration), and two variations of stereotypical manual driving (sudden stop, crawling forward). The AV was observed by 24 participants standing on the curb of the road in groups. After each passing of the AV, participants rated whether the car was driven manually or automatically, and the degree to which they liked the AV’s behaviour. Results showed that the playback manual trajectory was considered more manual than the other trajectory conditions. The stereotype automated ‘road centre tendency’ and ‘lane centre tendency’ trajectories received similar likeability ratings as the playback manual driving. An analysis of written comments showed that curve cutting was a reason to believe the car is driving automatically, whereas driving at a constant speed or in the centre was associated with automated driving. The sudden stop was the least likeable way to decelerate, but there was no consensus on whether this behaviour was manual or automated. It is concluded that AVs do not have to drive like a human in order to be liked.
DOI: 10.4121/uuid:6f781c05-c696-4d0e-9fc6-bbfb7247a19e
title: Supplementary data for the article: Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway.

description: Supplementary data for the article: Bazilinskyy, P., Larsson, P., Johansson, E., & De Winter, J. C. F. (2019). Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway. Acoustical Science and Technology, 40, 382-390.
DOI: 10.4121/12703346.v2
title: Supplementary data for the article: Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway.

description: Supplementary data for the article: Bazilinskyy, P., Larsson, P., Johansson, E., & De Winter, J. C. F. (2019). Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway. Acoustical Science and Technology, 40, 382-390.
DOI: 10.4121/12703346
title: Supplementary data for the article: Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway.

description: Supplementary data for the article: Bazilinskyy, P., Larsson, P., Johansson, E., & De Winter, J. C. F. (2019). Continuous auditory feedback on the status of adaptive cruise control, lane deviation, and time headway. Acoustical Science and Technology, 40, 382-390.
DOI: 10.4121/12715664.v2
title: Supplementary data for the paper: Risk perception - A study using dashcam videos and participants from different world regions.

description: Supplementary material for the paper: Bazilinskyy, P., Eisma. Y. B., Dodou, D., & De Winter, J. C. F. (in press). Risk perception: A study using dashcam videos and participants from different world regions. Traffic Injury Prevention.
DOI: 10.4121/uuid:cd649413-c707-4469-8c47-2e20a0ee1f87
title: Supplementary data for the paper: Risk perception - A study using dashcam videos and participants from different world regions.

description: Supplementary material for the paper: Bazilinskyy, P., Eisma. Y. B., Dodou, D., & De Winter, J. C. F. (in press). Risk perception: A study using dashcam videos and participants from different world regions. Traffic Injury Prevention.
DOI: 10.4121/12715664
title: Supplementary data for the paper: Risk perception - A study using dashcam videos and participants from different world regions.

description: Supplementary material for the paper: Bazilinskyy, P., Eisma. Y. B., Dodou, D., & De Winter, J. C. F. (in press). Risk perception: A study using dashcam videos and participants from different world regions. Traffic Injury Prevention.
DOI: 10.4121/uuid:6c02218f-cd2c-4eb2-8c93-1eeac44690ed
title: Supplementary material for Blind Driving papers

description: These folders contain supplementary materials for the following three papers * Blind Driving 1 (BD1): Bazilinskyy, P., Van der Geest, L., Van Leeuwen, S., Numan, B., Pijnacker, J., & De Winter, J. C. F. (2016). Blind driving by means of auditory feedback. 13th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Kyoto, Japan, 49, 525–530. * Blind Driving 2 (BD2): Bazilinskyy, P., Beaumont, C., Van der Geest, X., De Jonge, R., Van der Kroft, K., & De Winter, J. C. F. (2018). Blind driving by means of a steering-based predictor algorithm. In N. A. Stanton (Ed.), Advances in Human Aspects of Transportation (pp. 457–466). Springer International Publishing. * Blind Driving 3 (BD3): Bazilinskyy, P., Bijker, L., Dielissen, T., French, S., Mooijman, T., Peters, L., Happee, R., Dodou, D., & De Winter, J. C. F. (2019). Blind driving by means of the track angle error. 26th International Congress on Sound and Vibration, Montreal.
DOI: 10.4121/12702911.v2
title: Supplementary material for Blind Driving papers

description: These folders contain supplementary materials for the following three papers * Blind Driving 1 (BD1): Bazilinskyy, P., Van der Geest, L., Van Leeuwen, S., Numan, B., Pijnacker, J., & De Winter, J. C. F. (2016). Blind driving by means of auditory feedback. 13th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Kyoto, Japan, 49, 525–530. * Blind Driving 2 (BD2): Bazilinskyy, P., Beaumont, C., Van der Geest, X., De Jonge, R., Van der Kroft, K., & De Winter, J. C. F. (2018). Blind driving by means of a steering-based predictor algorithm. In N. A. Stanton (Ed.), Advances in Human Aspects of Transportation (pp. 457–466). Springer International Publishing. * Blind Driving 3 (BD3): Bazilinskyy, P., Bijker, L., Dielissen, T., French, S., Mooijman, T., Peters, L., Happee, R., Dodou, D., & De Winter, J. C. F. (2019). Blind driving by means of the track angle error. 26th International Congress on Sound and Vibration, Montreal.
DOI: 10.4121/12702911
title: Supplementary material for Blind Driving papers

description: These folders contain supplementary materials for the following three papers * Blind Driving 1 (BD1): Bazilinskyy, P., Van der Geest, L., Van Leeuwen, S., Numan, B., Pijnacker, J., & De Winter, J. C. F. (2016). Blind driving by means of auditory feedback. 13th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Kyoto, Japan, 49, 525–530. * Blind Driving 2 (BD2): Bazilinskyy, P., Beaumont, C., Van der Geest, X., De Jonge, R., Van der Kroft, K., & De Winter, J. C. F. (2018). Blind driving by means of a steering-based predictor algorithm. In N. A. Stanton (Ed.), Advances in Human Aspects of Transportation (pp. 457–466). Springer International Publishing. * Blind Driving 3 (BD3): Bazilinskyy, P., Bijker, L., Dielissen, T., French, S., Mooijman, T., Peters, L., Happee, R., Dodou, D., & De Winter, J. C. F. (2019). Blind driving by means of the track angle error. 26th International Congress on Sound and Vibration, Montreal.
DOI: 10.4121/uuid:ed63e704-ac75-4f96-a2d7-4c8e3b48b168
title: Supplementary data for the article: When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

description: Scripts and raw data for the article: Bazilinskyy, P., Kyriakidis, M., Dodou, D., & De Winter, J. C. F. (2019). When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents. Transportation Research Part F, 64, 184–195.
DOI: 10.4121/12718385.v2
title: Supplementary data for the article: When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

description: Scripts and raw data for the article: Bazilinskyy, P., Kyriakidis, M., Dodou, D., & De Winter, J. C. F. (2019). When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents. Transportation Research Part F, 64, 184–195.
DOI: 10.4121/12718385
title: Supplementary data for the article: When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

description: Scripts and raw data for the article: Bazilinskyy, P., Kyriakidis, M., Dodou, D., & De Winter, J. C. F. (2019). When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents. Transportation Research Part F, 64, 184–195.
DOI: 10.4121/15015390.v1
title: Data underlying the publication "Global civil aviation emissions estimates for 2017–2020 using ADS-B data"

description: 3-D gridded estimates of monthly global civil fixed-wing aircraft emissions in the period from 2017 to 2020 (inclusive) obtained using the openAVEM model according to the methodology described in the published manuscript. Partial emissions for the year 2016 calculated from non global coverage are also included. The data includes fuel burn and emissions of NOx (oxides of nitrogen), HC (unburned hydrocarbons), CO, nvPM (non-volatile particulate matter, mass), nvPM_N (number of nvPM particles). Main engine and APU fuel burn and emissions from landing and takeoff (LTO) and non-LTO phases of flight (climb, cruise, descent) are included. Rotocraft emissions, tire, runway and brake dust, ground support equipment (GSE) and other airport emissions are not included. This dataset also includes global sums of aircraft emissions for test cases used in the emission model's sensitivity analysis, region assignment for each grid cell, and surface area of each grid cell.
DOI: 10.4121/15015390
title: Data underlying the publication "Global civil aviation emissions estimates for 2017–2020 using ADS-B data"

description: 3-D gridded estimates of monthly global civil fixed-wing aircraft emissions in the period from 2017 to 2020 (inclusive) obtained using the openAVEM model according to the methodology described in the published manuscript. Partial emissions for the year 2016 calculated from non global coverage are also included. The data includes fuel burn and emissions of NOx (oxides of nitrogen), HC (unburned hydrocarbons), CO, nvPM (non-volatile particulate matter, mass), nvPM_N (number of nvPM particles). Main engine and APU fuel burn and emissions from landing and takeoff (LTO) and non-LTO phases of flight (climb, cruise, descent) are included. Rotocraft emissions, tire, runway and brake dust, ground support equipment (GSE) and other airport emissions are not included. This dataset also includes global sums of aircraft emissions for test cases used in the emission model's sensitivity analysis, region assignment for each grid cell, and surface area of each grid cell.
DOI: 10.4121/19666290.v1
title: Data belonging the publication: ‘A state of the art classification and review of parameters that affect the design, control, and operating strategies of order picking system’

description: This dataset includes a series of tables (Appendix) which mentioned in an academic article entitled "A state of the art classification and review of parameters that affect the design, control, and operating strategies of order picking system".
DOI: 10.4121/14096067.v1
title: Supplementary data for the paper 'Bio-inspired intent communication for automated vehicles'

description: Various external human-machine interfaces (eHMIs) have been proposed that communicate the intent of automated vehicles (AVs) to vulnerable road users. However, there is no consensus on which eHMI concept is most suitable for intent communication. In nature, animals have evolved the ability to communicate intent via visual signals. Inspired by intent communication in nature, this paper investigated three novel and potentially intuitive eHMI designs that rely on posture, gesture, and colouration, respectively. In an online crowdsourcing study, 1141 participants viewed videos featuring a yielding or non-yielding AV with one of the three bio-inspired eHMIs, as well as a green/red lightbar eHMI, a walk/don’t walk text-based eHMI, and a baseline condition (i.e., no eHMI). Participants were asked to press and hold a key when they felt safe to cross and to answer rating questions. Together, these measures were used to determine the intuitiveness of the tested eHMIs. Results showed that the lightbar eHMI and text-based eHMI were more intuitive than the three bio-inspired eHMIs, which, in turn, were more intuitive than the baseline condition. An exception was the bio-inspired colouration eHMI, which produced a performance score that was equivalent to the text-based eHMI when communicating ‘non-yielding’. Further research is necessary to examine whether these observations hold in more complex traffic situations. Additionally, we recommend combining features from different eHMIs, such as the full-body communication of the bio-inspired colouration eHMI with the colours of the lightbar eHMI.
DOI: 10.4121/13603787.v1
title: Data underlying the publication: Image-based size estimation of broccoli heads under varying degrees of occlusion

description: This publicly available dataset contains 1613 RGB-D images of field-grown broccoli plants. The dataset also includes the polygon and circle annotations of the broccoli heads. <br> <br> The broccoli heads in the images were subject to various degrees of natural and man-made leaf occlusion. The images were acquired in July and August 2020 on a broccoli field in Sexbierum (The Netherlands). The broccoli cultivar was Ironman. <br> <br> The dataset belongs to the paper “Image-based size estimation of broccoli heads under varying degrees of occlusion”. This paper has been published at Biosystems Engineering journal: https://doi.org/10.1016/j.biosystemseng.2021.06.001<br><br><br>
DOI: 10.4121/13603787.v3
title: Data underlying the publication: Image-based size estimation of broccoli heads under varying degrees of occlusion

description: This publicly available dataset contains 1613 RGB-D images of field-grown broccoli plants. The dataset also includes the polygon and circle annotations of the broccoli heads. <br> <br> The broccoli heads in the images were subject to various degrees of natural and man-made leaf occlusion. The images were acquired in July and August 2020 on a broccoli field in Sexbierum (The Netherlands). The broccoli cultivar was Ironman. <br> <br> The dataset belongs to the paper “Image-based size estimation of broccoli heads under varying degrees of occlusion”. This paper has been published at Biosystems Engineering journal: https://doi.org/10.1016/j.biosystemseng.2021.06.001<br><br><br>