# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 1 Introduction  
As the demand for waterway transport continues to grow, the inland shipping industry is rapidly expanding, making vessel safety and efficient navigation central to the sector's priorities. For instance, in the Yangtze River system, the total number of inland transport vessels reached 93,400 in 2022, with a combined deadweight of 125 million tons. This represents a 49% increase in carrying capacity compared to 2012, while the average tonnage of cargo ships rose by 129%. However, with the ongoing expansion of inland shipping, high-density traffic in waterways have introduced significant safety risks. Between 2018 and June 2022, a total of 563 waterway accidents occurred along the Yangtze River main line, with collisions accounting for 48.31% of these incidents [1]. These frequent traffic accidents result in substantial economic losses and decreased navigational efficiency, underscoring the urgent need to enhance existing waterway traffic awareness technologies and improve the service capabilities of inland vessels. Currently, traditional waterway traffic awareness services predominantly rely on vessel traffic service (VTS) systems to provide traffic control and navigational guidance,ensuring navigational safety. However, these services typically depend on a limited number of sensors as data sources, which introduces significant limitations in the complex inland waterway environment of today [2]. For instance, AIS receivers collect data on vessel position, speed, and heading, offering basic dynamic monitoring capabilities. Nevertheless, the transmission of AIS data is subject to latency, and in high-density traffic environments, data congestion can occur, compromising real-time performance [3]. Video monitoring data can directly capture the visual appearance and dynamic behavior of vessels, providing the advantage of obtaining detailed, fine-grained information. However, traditional video monitoring systems face significant limitations in today's complex inland waterway environment. In expansive waterway settings, multi-camera systems struggle to detect small vessels due to constraints in resolution, frame rate, and equipment performance. Additionally, complex background noise and dynamic occlusion further degrade the recognition accuracy of target detection algorithms [4]. Furthermore, a large volume of existing video data is often not effectively analyzed, processed, or utilized, and relying on manual monitoring for comprehensive surveillance has made real-time, efficient operation increasingly challenging. The limitations of relying on a single data source can no longer meet the service demands of today's complex inland waterway environment. As a result, effectively integrating data from diverse sensors has become crucial for enhancing the safety and efficiency of inland waterways. In recent years, the rapid advancement of deep learning technology has introduced new methodologies for waterway traffic perception. With its powerful feature extraction and pattern recognition capabilities, deep learning has led to significant breakthroughs in areas such as object detection, object tracking, and multi-source data fusion. Deep learning enables the processing of large-scale heterogeneous data from multiple sensors, such as AIS and video monitoring, to uncover potential correlations between data, facilitating the seamless integration of dynamic information and visual features and thereby providing a comprehensive perception of complex dynamic scenes. Currently, multi-source data fusion for waterway traffic situation awareness faces several challenges in practical applications. The format, temporal resolution, and spatial frequency of different data sources vary significantly. Additionally, environmental differences in waterways during data collection complicate the temporal and spatial synchronization of multi-source data. To address these challenges, this paper proposes a waterway traffic situation awareness method based on multi-source sensor data collected through an intelligent navigation marker system.