Explainable DensityBased Approach for SelfDriving Actions Classification This paper describes a new selforganizing neurofuzzy approach to autonomously learn interpretable models by selfdriving cars. A new explainable selforganizing architecture and a new densitybased feature selection method are proposed. These new approaches are used to classify different action states occurring from different selfdriving 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 0order fuzzy rules. The proposed densitybased 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 selforganizing 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 selfdriving 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 selfdriving performances. Results demonstrate that the proposed approach could surpass its stateoftheart competitors in terms of accuracy for this challenge multiclass classification problem.

Deep MultiTask Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safetycritical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semisupervised anomaly detection considering the imbalance of normal situations: In particular, driving data consists of multiple normal situations (e.g., right turn, going straight), some of which (e.g., Uturn) could be as rare as anomalous ones. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multitask learning (LSTM autoencoder and predictor) based approach that leverages domainknowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of realworld driving data and show improved performance over baselineexisting approaches.