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.

Exploiting Sparse Semantic HD Maps for SelfDriving Vehicle Localization In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry and LiDAR intensitybased localizers. This is important as selfdriving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensitybased approaches.