Radarbased Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles Radarbased road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a onevsone binarization paradigm are enriched by onevsall correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.

Machine LearningBased Roadside Vehicular Traffic Localization via Opportunistic Wireless Sensing Comprehensive Situational Awareness (SA) in mixed traffic environments (i.e., both autonomous and humanoperated platforms) is a critical requirement in addressing some of the challenges that hinder the deployment of autonomous vehicle (AV) systems onto roadways. In this paper, a novel framework that leverages machine learning techniques for utilizing Signals of Opportunity (SoO) for robust localization of all vehicles operating along a stretch of roadway is presented. By making use of ubiquitous wireless emissions from vehicles, the presented approach performs vehicle localization without any active participationassistance from vehicles thus making it a suitable candidate for SA in mixed traffic environments. Our simulation results show that given the road shape and number of vehicles present, observed 2D localization estimates generated by an arbitrary algorithm whose error is described by a Gaussian bivariate distribution with 10 meters covariance yields unbiased vehicle centroid estimates with less than one meter mean squared error by eight Kalman Filter (KF) iterations. A set of KFs for each vehicle are used to leverage the filtering of multiple estimates per vehicle per filter step to reduce measurement noise by averaging, while a clustering algorithm performs the dual role of forming KF set priors and classifying location estimates to their correct vehicle.