  The use of smart roadside infrastructure sensors is highly relevant for
future applications of connected and automated vehicles. External sensor
technology in the form of intelligent transportation system stations (ITS-Ss)
can provide safety-critical real-time information about road users in the form
of a digital twin. The choice of sensor setups has a major influence on the
downstream function as well as the data quality. To date, there is insufficient
research on which sensor setups result in which levels of ITS-S data quality.
We present a novel approach to perform detailed quality assessment for smart
roadside infrastructure sensors. Our framework is multimodal across different
sensor types and is evaluated on the DAIR-V2X dataset. We analyze the
composition of different lidar and camera sensors and assess them in terms of
accuracy, latency, and reliability. The evaluations show that the framework can
be used reliably for several future ITS-S applications.
