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MLCS: Laura Ruotsalainen, "Securing Pedestrians in Autonomous Traffic Ecosystems of Smart Cities"

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am – 10 am.

Securing Pedestrians in Autonomous Traffic Ecosystems of Smart Cities

Laura Ruotsalainen
Professor of Navigation and Positioning,
Finnish Geospatial Research Institute

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.


See the next talks at the seminar webpage.

Please spread the news and join us for our weekly habit of beginning the week by an interesting machine learning talk!