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An understanding and classification of driving scenarios are important fortesting and development of autonomous driving functionalities. Machine learningmodels are useful for scenario classification but most of them assume that datareceived during the testing are from one of the classes used in the training.This assumption is not true always because of the open environment wherevehicles operate. This is addressed by a new machine learning paradigm calledopen-set recognition. Open-set recognition is the problem of assigning testsamples to one of the classes used in training or to an unknown class. Thiswork proposes a combination of Convolutional Neural Networks (CNN) and RandomForest (RF) for open set recognition of traffic scenarios. CNNs are used forthe feature generation and the RF algorithm along with extreme value theory forthe detection of known and unknown classes. The proposed solution is featuredby 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 combinedwith extreme value theory is shown to be well suited for detecting unknownclasses. The proposed method has been tested on the highD and OpenTrafficdatasets and has demonstrated superior performance in various aspects comparedto existing solutions.

Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios