This work aims to address the challenges in autonomous driving by focusing on
the 3D perception of the environment using roadside LiDARs. We design a 3D
object detection model that can detect traffic participants in roadside LiDARs
in real-time. Our model uses an existing 3D detector as a baseline and improves
its accuracy. To prove the effectiveness of our proposed modules, we train and
evaluate the model on three different vehicle and infrastructure datasets. To
show the domain adaptation ability of our detector, we train it on an
infrastructure dataset from China and perform transfer learning on a different
dataset recorded in Germany. We do several sets of experiments and ablation
studies for each module in the detector that show that our model outperforms
the baseline by a significant margin, while the inference speed is at 45 Hz (22
ms). We make a significant contribution with our LiDAR-based 3D detector that
can be used for smart city applications to provide connected and automated
vehicles with a far-reaching view. Vehicles that are connected to the roadside
sensors can get information about other vehicles around the corner to improve
their path and maneuver planning and to increase road traffic safety.Comment: arXiv admin note: substantial text overlap with arXiv:2204.0013