  Today vehicles are becoming a rich source of data as they are equipped with
localization or tracking and with wireless communications technologies. With
the increasing interest in automated- or self- driving technologies, vehicles
are also being equipped with range measuring sensors (e.g., LIDAR, stereo
cameras, and ultrasonic) to detect other vehicles and objects in the
surrounding environment. It is possible to envision that such vehicles could
share their data with the transportation infrastructure elements (e.g., a
traffic signal controller) to enable different mobility and safety
applications. Data from these connected vehicles could then be used to estimate
the system state in real-time. This paper develops queue length estimators from
connected vehicles equipped with range measurement sensors. Simple
plug-and-play models are developed for queue length estimations without needing
ground truth queue lengths by extending the previous formulations. The proposed
method is simple to implement and can be adopted to cyclic queues at traffic
signals with known phase lengths. The derived models are evaluated with data
from microscopic traffic simulations. From numerical experiments, the QLE model
with range sensors improves the errors as much as 25% in variance-to-mean ratio
and 5\% in coefficient of variation at low less than 20% market penetration
rates.
