Bosch Internet of Things & Services Lab
Type
applied research project
Start Date
March 1, 2012
End Date
February 28, 2016
Status
ongoing
Keywords
Internet of things
Bosch
networked mobility
smart home
Description
The lab's mission is to find and test out business models for the internet of things & services (IoTS). In addition, the lab will be working to develop innovative and breakthrough internet-based products and services. The first major areas of research include networked mobility and smart home.
Leader contributor(s)
Partner(s)
Bosch
Funder
Topic(s)
-
Method(s)
-
Range
HSG Internal
Range (De)
HSG Intern
Eprints ID
218341
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Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Driver Identification via Brake Pedal Signals - A Replication and Advancement of Existing TechniquesType:conference paperJournal:IEEE International Conference on Intelligent Transportation SystemsVolume:21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The impact of numerical vs. symbolic eco-driving feedback on fuel consumption – A randomized control field trial(Elsevier Science, 2018-12); ; Type:journal articleJournal:Transportation Research Part D: Transport and EnvironmentVolume:65Scopus© Citations 39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Crowd Sensing Approach to Video Classification of Traffic Accident HotspotsDespite various initiatives over the recent years, the number of traffic accidents has been steadily increasing and has reached over 1.2 million fatalities per year world wide. Recent research has highlighted the positive effects that come from educating drivers about accident hotspots, for example, through in-vehicle warnings of upcoming dangerous areas. Further, it has been shown that there exists a spatial correlation between to locations of heavy braking events and historical accidents. This indicates that emerging accident hotspots can be identified from a high rate of heavy braking, and countermeasures deployed in order to prevent accidents before they appear. In order to contextualize and classify historic accident hotspots and locations of current dangerous driving maneuvers, the research at hand introduces a crowd sensing system collecting vehicle and video data. This system was tested in a naturalistic driving study of 40 vehicles for two months, collecting over 140,000km of driving data and 36,000 videos of various traffic situations. The exploratory results show that through applying data mining approaches it is possible to describe these situations and determine information regarding the involved traffic participants, main causes and location features. This enables accurate insights into the road network, and can help inform both drivers and authorities.Type:conference paperJournal:Lecture Notes in Computer Science (LNCS)Volume:14Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study(Elsevier Science, 2018-05); ; Scopus© Citations 31
