Estimating the fundamental traffic state variables, namely, flow, density and speed play a fundamental role in traffic engineering. Conventional traffic state estimation methods rely on fixed traffic sensors such as loop detectors, cameras and microwave vehicle detectors. Due to the high cost of conventional traffic sensors, traffic state data are usually obtained in a low-frequency and sparse manner. In contrast, the last decades have witnessed the breakthrough of automated vehicles (AVs). It is projected that more and more AVs will operate on roads in next several decades, and various sensors installed on the AVs, including, but are not limited to, LiDAR, radar, camera and stereovision, will be collecting massive data and perceiving the surrounding traffic states continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway network. In view of this, this paper leverages rich data collected through AVs to propose high-resolution ubiquitous traffic sensing methods. The idea is to use AVs to detect and track objects in the vicinity of those AVs, and the data-driven traffic state estimation algorithms are developed for different vehicle automation levels. The Next Generation Simulation (NGSIM) data is adopted to examine the accuracy and robustness of the proposed framework. Experimental results are compelling, satisfactory and interpretable. Sensitivity analysis regarding AV penetration rate, sensor configuration, and perception accuracy will also be studied. This study will help policymakers and private sectors (e.g Uber, Waymo) to understand the values of AVs in traffic operation and management.