Browsing by Author "Guo, Shaoqing"

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    An adaptive trajectory compression and feature preservation method for maritime traffic analysis
    (Elsevier Ltd, 2024-11-15) Guo, Shaoqing; Bolbot, Victor; Valdez Banda, Osiris; Department of Mechanical Engineering; Marine and Arctic Technology
    Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compression techniques have been widely investigated to remedy the challenge. However, conventional compression techniques such as Douglas-Peucker (DP) algorithm mainly depend on line simplification algorithms, falling short in accurately identifying and preserving crucial information within trajectories. Moreover, using kinematic information from AIS data has posed difficulties associated with compression threshold determination. Hence, an adaptive method capable of considering multiple information from AIS is required. In this paper, a Top-Down Kinematic Compression (TDKC) algorithm aimed at adaptive trajectory compression and feature preservation is proposed. By incorporating time, position, speed, and course attributes from AIS data, TDKC exploits a Compression Binary Tree (CBT) method to address the recursion termination problem and determine the threshold automatically. A case study was conducted to evaluate the performance of TDKC using AIS data from Gulf of Finland, where a comparison with conventional algorithms and their improved versions based on specific performance evaluation metrics was involved. The results demonstrate TDKC's superiority in facilitating maritime traffic analysis.
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    Identification of hazardous encounter scenarios using AIS data for collision avoidance system testing
    (2023) Guo, Shaoqing; Bolbot, Victor; BahooToroody, Ahmad; Valdez Banda, Osiris; Siow, Chee Loon; Department of Mechanical Engineering; Marine and Arctic Technology; Universiti Teknologi Malaysia
    The rise of artificial intelligence and advanced automation techniques have supported the development of Maritime Autonomous Surface Ships (MASS). Countries and companies are competing and collaborating to become leaders in this arising market. The Collision Avoidance System (CAS) replicates the human operator with its decision-making ability to ensure navigational safety of MASS. The CAS employs advanced algorithms to implement a wide spectrum of functions from collision avoidance to route optimization. However, the verification of the CAS dependability is highly reliant on the coverage of implemented scenarios during testing, which directly influences its trustworthiness. Scenarios in previous research from manually designed approaches have a limited coverage, while those from simulation-based approaches based on algorithms are disconnected from the scenarios occurring in the actual operational contexts. Others from real data-based approaches using Automatic Identification System (AIS) data propose an unbearably large number of scenarios. Considering that critical risk scenarios can constitute the basis for the development of CAS testing, this study proposes a method for identifying critical encounter scenarios based on AIS data. The method uses safety indices to identify hazardous encounter scenarios. Then, a muti-ship encounter scenario classification method based on COLREGs is proposed to categorize these scenarios. For each category, the risk value of each scenario is evaluated by Time-varying Risk Vectors (TRV). Scenarios with the lowest and highest risk are then used as representative for the whole. In this study, AIS data from Singapore Strait covering one month of operation is used for scenario identification. The results are discussed indicating good effectiveness in identifying critical scenarios in water area.