Augmented Play Spaces (APS) are (semi-) public environments where playful interaction isfacilitated by enriching the existing environment with interactive technology. APS canpotentially facilitate social interaction and physical activity in (semi-)public environments. Incontrolled settings APS show promising effects. However, people’s willingness to engagewith APSin situ, depends on many factors that do not occur in aforementioned controlledsettings (where participation is obvious). To be able to achieve and demonstrate thepositive effects of APS when implemented in (semi-)public environments, it is important togain more insight in how to motivate people to engage with them and better understandwhen and how those decisions can be influenced by certain (design) factors. TheParticipant Journey Map (PJM) was developed following multiple iterations. First,based on related work, and insights gained from previously developed andimplemented APS, a concept of the PJM was developed. Next, to validate and refinethe PJM, interviews with 6 experts with extensive experience with developing andimplementing APS were conducted. Thefirst part of these interviews focused oninfluential (design) factors for engaging people into APS. In the second part, expertswere asked to provide feedback on thefirst concept of the PJM. Based on the insightsfrom the expert interviews, the PJM was adjusted and refined. The Participant JourneyMap consists of four layers: Phases, States, Transitions and Influential Factors. There aretwo overarchingphases:‘Onboarding’and‘Participation’and 6statesa (potential)participant goes through when engaging with an APS:‘Transit,’‘Awareness,’‘Interest,’‘Intention,’‘Participation,’‘Finishing.’Transitionsindicate movements between states.Influential factorsare the factors that influence these transitions. The PJM supportsdirections for further research and the design and implementation of APS. Itcontributes to previous work by providing a detailed overview of a participant journeyand the factors that influence motivation to engage with APS. Notable additions are thedetailed overview of influential factors, the introduction of the states‘Awareness,’‘Intention’and‘Finishing’and the non-linear approach. This will support taking intoaccount these often overlooked, key moments in future APS research and designprojects. Additionally, suggestions for future research into the design of APS are given.
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This paper generalizes existing BRDF fitting algorithms presented in the literature that aims to find a mapping of the parameters of an arbitrary source material model to the parameters of a target material model. A material model in this context is a function that maps a list of parameters, such as roughness or specular color, to a BRDF. Our conversion function approximates the original model as close as possible under a chosen similarity metric, either in physical reflectivities or perceptually, and calculates the error with respect to this conversion. Our conversion function imposes no constraints other than that the dimensionality of the represented BRDFs match.
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Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
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20 lectoren van 11 brede hogescholen zijn gestart met een netwerk gericht op het scheppen van synergie in onze bijdrage aan de Smart Industry beweging. Het platform is multidisciplinair samengesteld; technische, organisatorische en economische innovatie. Doel is versterking samenwerking en meer impact van hogescholen op deze belangrijke beweging. We richten ons met name op de zogeheten fieldlabs (inmiddels 15 stuks) en de daaraan deelnemende (MKB) bedrijven.
