  The last decades have witnessed the breakthrough of autonomous vehicles
(AVs), and the perception capabilities of AVs have been dramatically improved.
Various sensors installed on 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 networks. In contrast,
conventional traffic sensing 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 view of this, this paper leverages rich
data collected through AVs to propose the high-resolution traffic sensing
framework. The proposed framework estimates the fundamental traffic state
variables, namely, flow, density and speed in high spatio-temporal resolution,
and it is developed under different levels of AV perception capabilities and
low AV market penetration rate. The Next Generation Simulation (NGSIM) data is
adopted to examine the accuracy and robustness of the proposed framework.
Experimental results show that the proposed estimation framework achieves high
accuracy even with low AV market penetration rate. 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, especially the values of
massive data collected by AVs, in traffic operation and management.
