Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/40340
Full metadata record
DC FieldValueLanguage
dc.date.accessioned2023-06-08T10:05:54Z-
dc.date.available2023-06-08T10:05:54Z-
dc.date.issued2023-
dc.date.submitted2023-06-08T08:22:06Z-
dc.identifier.citationZenodo. 10.5281/zenodo.7684848 https://zenodo.org/record/7684848-
dc.identifier.urihttp://hdl.handle.net/1942/40340-
dc.description.abstractThe overall objective of the i-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the i-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/ This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.-
dc.description.sponsorshipSafety tolerance zone calculation and interventions for driver-vehicle-environment interactions under challenging conditions. European Commission. awardNumber:814761. https://doi.org/10.13039/100010661-
dc.language.isoen-
dc.publisherZenodo-
dc.subject.classificationTransportation impact analysis-
dc.subject.otherNaturalistic driving data-
dc.subject.otheri-DREAMS H2020 EU Project-
dc.subject.otherDriving behaviour-
dc.subject.otherRoad Safety-
dc.subject.otherDriving assistance systems-
dc.titlei-Dreams H2020 EU Project: Sample dataset-
dc.typeDataset-
local.bibliographicCitation.jcatDS-
dc.description.version1.0-
dc.rights.licenseCreative Commons Attribution 4.0 International (CC-BY-4.0)-
dc.identifier.doi10.5281/zenodo.7684848-
dc.identifier.urlhttps://zenodo.org/record/7684848-
local.provider.typedatacite-
local.uhasselt.internationalno-
local.contributor.datacreatorBRIJS, Tom-
local.contributor.datacuratorBRIJS, Tom-
local.contributor.rightsholderBRIJS, Tom-
local.format.extent20.0 Mb-
local.format.mimetypeComma-separated values (CSV)-
local.format.mimetype.docx-
local.contributororcid.datacreator0000-0003-2622-4398-
local.contributororcid.datacurator0000-0003-2622-4398-
local.contributororcid.rightsholder0000-0003-2622-4398-
local.contributingorg.datacreatorHasselt University-
local.contributingorg.datacuratorHasselt University-
local.contributingorg.rightsholderHasselt University-
dc.rights.accessOpen Access-
item.fulltextNo Fulltext-
item.fullcitationBRIJS, Tom (2023) i-Dreams H2020 EU Project: Sample dataset. Zenodo. 10.5281/zenodo.7684848 https://zenodo.org/record/7684848.-
item.accessRightsClosed Access-
item.contributorBRIJS, Tom-
crisitem.discipline.code02010707-
crisitem.discipline.nameTransportation impact analysis-
crisitem.discipline.pathEngineering and technology > Civil and building engineering > Infrastructure, transport and mobility engineering > Transportation impact analysis-
crisitem.discipline.pathandcodeEngineering and technology > Civil and building engineering > Infrastructure, transport and mobility engineering > Transportation impact analysis (02010707)-
crisitem.license.codeCC-BY-4.0-
crisitem.license.nameCreative Commons Attribution 4.0 International (CC-BY-4.0)-
Appears in Collections:Datasets
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.