With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7.

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively.

With the continuous development of global intelligent shipping technology, in fields such as virtual testing of intelligent ships and crew training and assessment, there is an urgent need for a highly realistic model to reproduce the driver’s bumpy feeling of ship drivers. Due to the limited travel of the six-degree-of-freedom platform, the platform is unable to provide continuous acceleration during the simulation of the driver’s body sensation in the three degrees of freedom of the ship, namely, sway, surge, and yaw. To overcome the above problems, a six-degree-of-freedom motion model of ships is constructed under low sea conditions based on the MMG-separated ship motion model and the FFT wave simulation method. Secondly, the otolith model and the semicircular canal model are introduced to establish a human body perception deception mechanism. The gravity is transferred by using the deflection angles of roll and pitch to extend the acceleration sensation in the three degrees of freedom of sway, surge, and yaw. Finally, through the real ship rotation and Z-shaped test experiments, the simulation trajectory, real ship attitude, and platform motion data are compared to verify the effectiveness of the established method. To simplify the research, under the low sea conditions where the three degrees of freedom of heave, roll, and pitch are ignored, the virtual ship simulation trajectory based on the above method is basically consistent with the real ship, and the correlation between the platform and the real ship body-sensing data is at least 81.2%. Through scoring the simulation driving body-sensing reproduction experience, it is proven that the above method can achieve a better body-sensing reproduction effect on the six-degree-of-freedom platform.