Explainable Density-Based Approach for Self-Driving Actions Classification
Eduardo Almeida Soares, Plamen Parvanov Angelov, Dimitar Filev, Bruno Sielly Jales Costa, Marcos P. Gerardo Castro, Subramanya P. Nageshrao · 2019
This paper describes a new self-organizing neuro-fuzzy approach to autonomously learn interpretable models by self-driving cars. A new explainable self-organizing architecture and a new density-based feature selection method are proposed. These new approaches are used to classify different action states occurring from different self-driving conditions. The proposed approach is able to provide human understandable IF ... THEN rules representation due to its learning engine which is composed of a massively parallel set of 0-order fuzzy rules. The proposed density-based feature selection method is based on the ranking of the densities of each feature in the data space, and takes advantage of the parallel characteristic of the proposed explainable self-organizing approach to create individualized subsets of features per class. The main goal of both proposed methods is to provide highly accurate models with high transparency, interpretability, and explainability for self-driving vehicles. In order to validate our proposal, experiments were realized using a real dataset provided by Ford Motor Company. The dataset contains different driving states occurring during self-driving performances. Results demonstrate that the proposed approach could surpass its state-of-the-art competitors in terms of accuracy for this challenge multiclass classification problem.