Computer Science and Engineering
 Gothenburg University | Chalmers

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City Safety Event Classification using Machine Learning

N. Jurczyńska

Master's Thesis, University of Göteborg, Jun 2019.

City safety technology aims to reduce vehicle collisions using activated warnings and braking based on automated detection of environmental threats. However, au- tomatic detection of tentative collisions may differ from driver perception, leading to false positive activations. This work analyses vehicle on-board sensor suite in the event of City Safety activations and learns the optimal features responsible for activation classifications. From the 152 activation events, 8 second multivariate logs containing 316 signals are mined to achieve around 98% of ROC_AUC score in event classification. Thus, supervised and semi-supervised classifications signif- icantly bridge the gap between automated and human perception for autonomous driving functionalities.