  Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave
radar sensors for large-scale traffic monitoring due to their weatherproof
functionality, long sensing range and low manufacturing cost. However, the
localization method using external measurement devices has limitations in urban
environments. Furthermore, if the sensor mount exhibits changes due to
environmental influences, they cannot be corrected when the measurement is
performed only during the installation. In this paper, we propose
self-localization of roadside radar data using Extended Object Tracking (EOT).
The method analyses both the tracked trajectories of the vehicles observed by
the sensor and the aerial laser scan of city streets, assigns labels of driving
behaviors such as "straight ahead", "left turn", "right turn" to trajectory
sections and road segments, and performs Semantic Iterative Closest Points
(SICP) algorithm to register the point cloud. The method exploits the result
from a down stream task -- object tracking -- for localization. We demonstrate
high accuracy in the sub-meter range along with very low orientation error. The
method also shows good data efficiency. The evaluation is done in both
simulation and real-world tests.
