Abstract: Future smart cities must be based on sustainable principles; they must provide improved quality of life for all citizens, be safe and as emission-free as possible. This goal requires radical reforms in the traffic, namely creation of automated ground vehicle ecosystem and relocating parts of the transportation of goods, probably also human, to the airspace by using Unmanned Aerial Vehicles (UAVs). Pedestrians and bicyclists must be included in traffic monitoring and controlling actions, a fact that has been largely neglected so far in the discussion
about automated traffic. All these goals demand development of sophisticated spatiotemporal data analysis methods. In order to implement a functional traffic ecosystem assuring safe cooperation of
all these actors, knowledge of their position, ability to predict their movements, effects caused by changes of the operation environment and capability to fuse all this information together is crucial.

The most challenging actors from the navigation perspective are pedestrians. Their motion is unrestricted, they spend a big portion of time indoors and they have strict demands for navigation equipment. This talk will give a glimpse to the research goals of the Spatiotemporal Data Analysis research group and will look a bit more into a specific application of infrastructure-free pedestrian navigation and especially user motion recognition via machine learning to improve the navigation result.

Speaker: Laura Ruotsalainen

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University