  An understanding and classification of driving scenarios are important for
testing and development of autonomous driving functionalities. Machine learning
models are useful for scenario classification but most of them assume that data
received during the testing are from one of the classes used in the training.
This assumption is not true always because of the open environment where
vehicles operate. This is addressed by a new machine learning paradigm called
open-set recognition. Open-set recognition is the problem of assigning test
samples to one of the classes used in training or to an unknown class. This
work proposes a combination of Convolutional Neural Networks (CNN) and Random
Forest (RF) for open set recognition of traffic scenarios. CNNs are used for
the feature generation and the RF algorithm along with extreme value theory for
the detection of known and unknown classes. The proposed solution is featured
by exploring the vote patterns of trees in RF instead of just majority voting.
By inheriting the ensemble nature of RF, the vote pattern of all trees combined
with extreme value theory is shown to be well suited for detecting unknown
classes. The proposed method has been tested on the highD and OpenTraffic
datasets and has demonstrated superior performance in various aspects compared
to existing solutions.
