Vehicle trajectory prediction (VTP) is essential for microscopic traffic risk assessment, autonomous vehicle navigation, and traffic behavior analysis. Related research leveraging learning-based methodologies has yielded notable success on various benchmark trajectory datasets. However, these models often experience performance degradation when faced with dynamic changes in traffic conditions such as vehicle density, road types, and weather conditions, as they have not been exposed to these variations during the training process. To effectively address the need for real-time adaptation in dynamic traffic scenarios, we propose a novel framework titled self-evolving spatial-temporal directed graph neural network (SE-STDGNN). This model utilizes evolving graph convolution networks (EvolveGCNs) to aggregate spatial-temporal features of vehicles and their neighbors, which are then utilized by a trajectory prediction module to forecast future trajectories. Further, a self-evolving mechanism is introduced to adjust model parameters dynamically in the real-time operation. The efficacy of SE-STDGNN is validated using the public vehicle trajectory dataset AD4CHE.

In recent years, significant progress has been made in the field of underwater image enhancement (UIE). However, its practical utility for high-level vision tasks, such as underwater object detection (UOD) in Autonomous Underwater Vehicles (AUVs), remains relatively unexplored. It may be attributed to several factors: (1) Existing methods typically employ UIE as a pre-processing step, which inevitably introduces considerable computational overhead and latency. (2) The process of enhancing images prior to training object detectors may not necessarily yield performance improvements. (3) The complex underwater environments can induce significant domain shifts across different scenarios, seriously deteriorating the UOD performance. To address these challenges, we introduce EnYOLO, an integrated real-time framework designed for simultaneous UIE and UOD with domain-adaptation capability. Specifically, both the UIE and UOD task heads share the same network backbone and utilize a lightweight design. Furthermore, to ensure balanced training for both tasks, we present a multi-stage training strategy aimed at consistently enhancing their performance. Additionally, we propose a novel domain-adaptation strategy to align feature embeddings originating from diverse underwater environments. Comprehensive experiments demonstrate that our framework not only achieves state-of-the-art (SOTA) performance in both UIE and UOD tasks, but also shows superior adaptability when applied to different underwater scenarios. Our efficiency analysis further highlights the substantial potential of our framework for onboard deployment.

Autonomous Marine Vehicles (AMVs) have been widely used in many critical tasks such as surveillance, patrolling, marine environment monitoring, and hydrographic surveying. However, most typical AMVs cannot meet the diverse demands of different marine tasks. In this article, we design a new type of remote-controlled hydrofoil marine vehicle, named Sea-U-Foil, which is suitable for different marine scenarios. Sea-U-Foil features three distinct locomotion modes, displacement mode, foilborne mode, and submarine mode, which enable the platform flexible mobility, high-speed and high-load capacities, and superior concealment. Specifically, the submarine mode makes Sea-U-Foil unique among previous studies. In addition, the performance of Sea-U-Foil in foilborne mode outperforms those of most current unmanned surface vehicles (USVs) in terms of speed and payload. To the best of our knowledge, we are the first to introduce a new type of AMV that can work in displacement mode, foilborne mode, and submarine mode. We elaborate on the design principles and methodologies of Sea-U-Foil first, then validate the effectiveness of its tri-modal locomotion through extensive experiments.

This paper presents a new modeling method based on signal flow graph correlation matrix. Firstly, according to the signal flow graph, a signal tree can be selected. Based on the signal tree, a signal branch and the node can be added, finding independent loop - loop incidence matrix. The correlation matrix can determine the relevance of the loop, and help seek its loop gain. Moreover, mathematical model can be figured out by using Mason formula. Theoretical analysis and experimental results show that when the nodes increase, this method is simpler and the meaning of the formula is clearer. In comparison to existing methods, our method has obvious advantages in calculation and expression. Our method makes the model of the system more accurate, and the modeling process becomes faster and more convenient.