# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Probabilistic Methods
Based on this data, a naive Bayes classifier uses the MES schedule and real-time position data to predict human movement intentions. The abstract activity modelling ensures that only a few training datasets are required for implementation, making the approach suitable for rapidly changing manufacturing environments, such as in flexible manufacturing. The model was also validated in a special laboratory. Said laboratory consisted of several workstations, a door to another workstation, a cafeteria, and a door that marked the end of a job. The key innovation lies in the introduction of a schedule-based approach for human trajectory prediction in manufacturing, leveraging real-time data from Manufacturing Execution Systems (MES). This method optimizes trajectory predictions by narrowing potential destinations based on current production steps. However, the model's primary limitation is its oversimplified view of human behaviour, assuming movements are task-driven. Additionally, its dependency on MES and real-time locating systems may restrict its broader applicability. For future endeavours, an integration of more contextual information should be made, especially for reasons of deviations from planned schedules and for exploring the potential of the human-digital twin domain and context middleware. Another publication by [PERSON] et al. [73] proposes a sequential similarity-based prediction approach that combines spatial and semantic contexts into a unified framework. The proposed method is evaluated using a real-world dataset from a large shopping mall that the authors hand-collected. The results show that it outperforms baseline methods and is suitable for real-world scenarios. The primary innovation is the introduction of a sequential similarity-based prediction approach that seamlessly integrates both spatial and semantic contexts within a single framework. This method is specifically tailored for indoor environments, which are rich in spatial-semantic information but also present unique constraints. However, a potential limitation is the model's reliance on a specific dataset from a large shopping mall, which might not generalize to all indoor environments. For future work, it would be beneficial to test the model in diverse indoor settings and explore refinements based on different spatial-semantic configurations. In their comprehensive study, [PERSON] et al. [74] present a unified framework, known as THOMAS, for the prediction of multi-agent trajectories. This innovative framework is aimed at providing efficient and consistent multi-mode predictions for multiple agents' trajectories. A unified model architecture for simultaneous estimation of future agent heatmaps is presented, using hierarchical and sparse image generation to enable fast and memory-efficient inference. A trajectory recombination model is proposed that takes a set of predicted trajectories for each agent as input and produces a reordered recombination that is consistent. The recombination module ensures that initially independent modalities are realigned to avoid collisions and maintain consistency. The results were presented for the Interaction multi-agent prediction challenge (Interaction 1.2 dataset [75]), showing that the proposed framework reached first place on the online test leaderboard. The primary innovation lies in the introduction of THOMAS. This framework uniquely combines a unified model architecture for simultaneous estimation of future agent heatmaps with hierarchical and sparse image generation, ensuring rapid and memory-efficient inference. Furthermore, THOMAS introduces a novel trajectory recombination model. This model, by taking sets of predicted trajectories for each agent, produces a reordered recombination that ensures trajectories are consistent and collision-free. It would be essential to consider the framework's generalizability across different datasets and scenarios.