Ramp merging areas, as critical nodes for intelligent vehicle traffic, are characterized by high dynamic interaction and strong uncertainty, making it difficult for traditional path planning algorithms to balance safety, real-time performance, and smoothness in such complex scenarios. To address these challenges, a path planning algorithm is proposed integrating an improved artificial potential field and line-of-sight A^*. Firstly, a piecewise potential field model comprising guidance, obstacle avoidance, and boundary constraints is constructed. A dynamic weight adjustment mechanism is also introduced to adapt to the kinematic requirements of vehicles at different merging stages, efficiently resolving the unreachable target problem of traditional potential field methods.Secondly, a physical line-of-sight check mechanism incorporating safety thresholds is embedded into the A^* algorithm. By utilizing a non-axial connection strategy to prune redundant nodes, the node expansion direction is optimized to enhance search efficiency. Finally, the improved potential field is mapped as the heuristic cost function of the A^* algorithm, achieving a deep coupling of global path planning and local risk perception. Simulation results demonstrate that, compared with traditional A^*, the Rapidly-exploring Random Tree Star (RRT^*) algorithm, and existing analogous fusion schemes, the runtime of the proposed algorithm is reduced by 70.8%, the minimum obstacle avoidance distance is increased by 106.6%, and lateral jerk is decreased to 0.62 m/s³. The proposed algorithm effectively enhances the safety and stability of trajectory planning, demonstrating its efficiency and robustness in complex merging scenarios.

A lightweight unmanned aerial vehicle (UAV) aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size, complex background, and dense distribution in images from the perspective of UAV aerial photography. This algorithm improves the Mosaic data augmentation method and overall network structure, and adds a small object detection head. At the same time, a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process. Based on the 4.0 version of YOLOv5s, experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET. The results showed that the improved model reduced the number of parameters by 48% and the computational complexity by 26% compared to the original model, and mAP@0.5 improved by 4.9 percentage points, mAP@0.5 0.95 increased by 3.3 percentage points, effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.