# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
Its purpose is to predict the next position of the vessel based on its current position in real time. Validation was performed on a dataset consisting of the location of vessels. These data most likely belong to the authors' usecase, since they have not provided any other information otherwise. The innovation lies in the model's three-fold approach: training, transfer learning, and inference. The training phase transforms ship trajectories into normalized features, optimized via a genetic algorithm. The transfer learning taps into a repository of pre-trained models. A limitation is the model's reliance on a dataset possibly proprietary to the authors, without referencing external sources. For future work, there is a potential in refining RNNs for trajectory analysis. This suggests an exploration of various optimization techniques, RNN types such as Gated Recurrent Units, and the integration of Attention mechanisms. A modular deep learning structure that factors in diverse inputs, such as weather or nearby objects, should be considered. This could enhance prediction accuracy. Another promising model was presented by [PERSON] et al. [67]. The authors proposed a new approach to the problem of human trajectory forecasting, called Y-net, which is an inherently multimodal problem with various sources of uncertainty. They proposed dividing uncertainty into epistemic and aleatoric sources and modelling them separately using multimodality in long-term goals, landmarks, and paths. They also introduced a new long-term trajectory forecasting setting with a prediction horizon of up to a minute, which is an order of magnitude longer than in previous work. Finally, they presented a new trajectory forecasting network called Y-net. Y-net operates using the proposed epistemic and aleatoric structure to generate differentiated trajectory forecasts. Those forecasts are particularly relevant at long prediction horizons. Moreover Y-net maintains compatibility with the scene. They showed that Y-net significantly outperforms state-of-the-art approaches for both short and long prediction horizons on the Stanford Drone, ETH/UCY, and Intersection Drone datasets. Accordingly, the authors achieved the development of a new approach to improve the accuracy of human trajectory prediction, especially for long-term prediction horizons. Innovation that authors introduced is their Y-net, which divides uncertainty into epistemic and aleatoric sources, modelling them with multimodality in long-term goals, landmarks, and paths. This distinction allows for more precise trajectory predictions, especially over extended periods. The authors also pioneered a new long-term trajectory forecasting setting, extending the prediction horizon to up to a minute, significantly longer than prior models. However, the research does not delve into potential limitations or challenges faced during the model's development. For future endeavors, the significant performance improvements suggest further exploration into refining Y-net's components, potentially integrating additional data sources or expanding its applicability to diverse real-world scenarios. In a significant contribution, [PERSON] et al. [68] proposed a model known as Trajectron++. This innovative model, characterized by its modular and recursive graph structure, is designed to predict multi-agent trajectories. It does so by considering the dynamics of the agents and incorporating heterogeneous data, including semantic maps. Trajectron++ is specifically designed to integrate with robot planning and control frameworks and can produce predictions that are optionally conditioned on ego-agent motion plans. The authors showcased the effectiveness of Trajectron++ using several real-world trajectory prediction datasets. These datasets posed significant challenges. However, Trajectron++ outperformed various state-of-the-art deterministic and generative methods, underscoring its effectiveness.