For the future of automated driving, it will be desirable that vehicles will be able to react to their surroundings. These vehicles will therefore have to be able to automatically detect traffic events. In order to develop such algorithms, large amounts of data will have to be dissected into different traffic events. TNO have developed algorithms for this purpose, regarding turn and lane change detection using only basic in car sensors. This thesis is about the optimization of these algorithms. The uncertainty regarding these detectors is expressed using probabilistic classifiers.

In the presentation the motivation for such detectors, the data set that is used, the methods that are used to optimize these algorithms and the methods used to express the uncertainty will be discussed.