  Large driving datasets are a key component in the current development and
safeguarding of automated driving functions. Various methods can be used to
collect such driving data records. In addition to the use of sensor equipped
research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure
sensor technology offers another alternative. To minimize object occlusion
during data collection, it is crucial to record the traffic situation from
several perspectives in parallel. A fusion of all raw sensor data might create
better conditions for multi-object detection and tracking (MODT) compared to
the use of individual raw sensor data. So far, no sufficient studies have been
conducted to sufficiently confirm this approach. In our work we investigate the
impact of fused LiDAR point clouds compared to single LiDAR point clouds. We
model different urban traffic scenarios with up to eight 64-layer LiDARs in
simulation and in reality. We then analyze the properties of the resulting
point clouds and perform MODT for all emerging traffic participants. The
evaluation of the extracted trajectories shows that a fused infrastructure
approach significantly increases the tracking results and reaches accuracies
within a few centimeters.
