Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios

Almeida Soares, Eduardo and Angelov, Plamen Parvanov and Costa, Bruno Sielly Jales and Castro, Marcos (2019) Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios. In: 2019 International Joint Conference on Neural Networks (IJCNN) :. IEEE, HUN, pp. 1-8. ISBN 9781728119861

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Abstract

This paper presents an actively semi-supervised multi-layer neuro-fuzzy modeling method, ASSDRB, to classify different lighting conditions for driving scenes. ASSDRB is composed of a massively parallel ensemble of AnYa type 0-order fuzzy rules. It uses a recursive learning algorithm to update its structure when new data items are provided and, therefore, is able to cope with nonstationarities. Different lighting conditions for driving situations are considered in the analysis, which is used by self-driving cars as a safety mechanism. Differently from mainstream Deep Neural Networks approaches, the ASSDRB is able to learn from unseen data. Experiments on different lighting conditions for driving scenes, demonstrated that the deep neuro-fuzzy modeling is an efficient framework for these challenging classification tasks. Classification accuracy is higher than those produced by alternative machine learning methods. The number of algebraic calculations for the present method are significantly smaller and, therefore, the method is significantly faster than common Deep Neural Networks approaches. Moreover, DRB produced transparent AnYa fuzzy rules, which are human interpretable.