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On Wednesday, January 25, Waleed Nauman Siddiqui will present intermediate results of his master's thesis with is done in cooperation with Institute for Driving Assistance and Connected Mobility (IFM) University of Applied Sciences, Kempten:

Spatiotemporal Clustering of Driving Situations Using Unsupervised Learning for Sensor Data Encoding

Abstract:

All drivers have their own habitual choice of driving style but these driving styles also vary under certain environmental scenarios. To improve the functionality of advanced driver systems, the information regarding the surrounding is of utmost importance. To get a better understanding of different environmental scenarios, this thesis aims to develop a system that can differentiate environmental scenes. Images and point clouds of different scenes are gathered from a driving vehicle. Using unsupervised manifold learning, the dimensionality of the raw sensor data is reduced to a 2D mapping and then clustered into representative driving situations.