Abstract
Providing a driver with information on risky events in advance is crucial for safer and more comfortable driving. This paper presents an approach to automated classification of near-miss situations for providing more useful information to drivers. Specifically, based on probe-car data, we introduce a statistical machine learning approach to the classification task. Experimental results show that our method is capable of identifying effective features from the data and is promising in near-miss pattern classification.
