# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 6 Discussion
### Future Work  
This paper has achieved significant results in the field of inland waterway traffic situation awareness, but there is still room for further improvement.  
Integrating more data sources to achieve refined perception.  
Although the multi-source data fusion method employed in this study significantly enhances perception capabilities in a waterway, the limited data coverage restricts the system's performance in varying traffic densities and waterway environments. Future work will incorporate a broader range of data sources, enabling the system to maintain robust fusion performance across different types of waterways and environmental conditions. Additionally, the inclusion of diverse data sources will mitigate the over-reliance on the quality and accuracy of a single data source.  
Enhancing the detection capability of fully occluded targets.  
To address the issue of target detection in cases of complete occlusion, future research could explore the deployment of distributed sensor networks, utilizing sensor data from multiple perspectives for cross-validation and supplementation. This approach would compensate for the limitations of single-perspective detection and further enhance the detection capability in scenarios of full occlusion.  
Challenges in the deployment of intelligent buoy systems  
Despite the strong performance and promising application prospects of the proposed intelligent beacon system, several challenges remain in real-world environments. Firstly, the initial deployment cost of the system is high, particularly when integrating various sensors, image processing units, and high-performance computing platforms. The substantial initial cost and complexity associated with building and maintaining the infrastructure may hinder widespread adoption, especially in underdeveloped regions. Furthermore, as the number of sensors, cameras, and other devices in the system increases, the volume of data also rises sharply. In high-density traffic environments or complex waterway conditions, the system's processing capacity and storage may struggle to respond to the data flow quickly, potentially leading to overload or delays that affect the system's real-time monitoring and accuracy.  
Future ship-shore coordination will rely on large model technologies to create a highly intelligent navigation service and traffic management network. By deploying large-scale systems on navigational aids such as beacons, real-time vessel navigation data can be processed, and by analyzing the surrounding environment, navigation recommendations and hazard warnings can be generated. Meanwhile, the shore-based traffic management system can obtain real-time information from beacons, dynamically adjust the waterway traffic flow, and optimize vessel scheduling. This intelligent collaborative model will closely integrate vessels with shore management systems, enhancing navigation safety, optimizing shipping efficiency, and driving the digital transformation of waterway management while providing more accurate, flexible, and secure support for future smart shipping.