The analysis and prediction of agent trajectories are crucial for decision-making processes in intelligent systems, with precise short-term trajectory forecasting being highly significant across a range of applications. Agents and their social interactions have been quantified and modeled by researchers from various perspectives; however, substantial limitations exist in the current work due to the inherent high uncertainty of agent intentions and the complex higherorder influences among neighboring groups. SocialMOIF is proposed to tackle these challenges, concentrating on the higher-order intention interactions among neighboring groups while reinforcing the primary role of first-order intention interactions between neighbors and the target agent. This method develops a multi-order intention fusion model to achieve a more comprehensive understanding of both direct and indirect intention information. Within SocialMOIF, a trajectory distribution approximator is designed to guide the trajectories toward values that align more closely with the actual data, thereby enhancing model interpretability. Furthermore, a global trajectory optimizer is introduced to enable more accurate and efficient parallel predictions. By incorporating a novel loss function that accounts for distance and direction during training, experimental results demonstrate that the model outperforms previous state-of-the-art baselines across multiple metrics in both dynamic and static datasets.

The objective of dehazing images is to enhance a hazy image by removing the atmospheric haze and restoring it to a clear, haze-free image. Previous studies often used simple deep learning methods combined with dehazing physical models, while neglecting the significance behind the parameters of the physical models. They did not employ appropriate deep learning methods to learn the parameters, which in turn hindered the effective restoration of the color and texture details in the images. To address the above issues, we propose a physical perception network (PPnet) for color and texture recovery. PPnet includes the following two major parts: the High-low frequency physical perception module(FPP) and the Attention-based physical estimation module(APE). The FPP module employ trainable filters to produce feature maps at both high and low frequencies, which are then applied to two identical single-parameter dehazing physical models. This module takes into greater consideration the contours and detailed features of images at different frequencies, and corrects the images through a physical model. Furthermore, in the physical model, we find that different parameters represent different physical meanings. Some parameters express global features while others represent local features. We propose the APE module, which utilized the pixel and channel attention method to simulate the physical parameter representation. Thus, it more accurately considers the physical significance of the parameters. PPnet exhibits a notable enhancement on the SOTS dataset the and HSTS dataset compared to existing state-of-the-art techniques.