With the rapid development of autonomous driving technologies, intelligent ports,
particularly autonomous logistics, have become the focus of industry attention.
Ensuring safe and efficient operations require port management systems to
perceive and predict the behaviors of people and vehicles. In the filed of
behavior perception, research efforts have primarily focused on the detection
and tracking of vehicles, pedestrians, and obstacles under various sensor
configurations. Common approaches include vision-based, LiDAR-based, and
multi-sensor fusion methods. In terms of behavior prediction, existing
approaches can be broadly categorized into four paradigms: model-driven,
data-driven, environment-assisted, and anomaly prediction methods. Model-driven
approaches rely on physical and motion models, while data-driven approaches
utilize deep learning techniques. Environment-assisted approaches integrate
prior knowledge such as maps, while anomaly prediction focus on identifying
unexpected behaviors to improve safety. Our study shows that Probabilistic 3D
significantly outperforms traditional methods in both detection and tracking
accuracy for unmanned port logistics scenarios. By integrating 2D and 3D data,
our method improves the detection of small obstacles such as pedestrians,
bicycles, and motorcycles, addressing key limitations of pure 3D-based
approaches. This enhancement makes autonomous logistics systems more reliable
and robust in complex environments. Future work will focus on refining
multi-sensor fusion techniques and developing adaptive learning models to
further enhance system adaptability in smart port operations.