# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
The model was validated using six synthetic datasets: (1) pure Newtonian trajectories; (2) noisy Newtonian trajectories; (3) trajectories that are looped, circled, and used other geometric curves; (4) an interactive trajectory model using HMM; (5) a set based on reinforcement learning--the agent environment and their movement; and (6) a synthetic SDD. The innovative aspect of the this work lies in the enhancement of the PECNet model through the introduction of the SIREN model. This new approach incorporates three pivotal advancements: the adoption of CMA-ES for optimization, the implementation of cyclic annealing for stable variational autoencoder training, and the integration of an additional generative model to produce a multimodal output, resulting in a more reliable prediction metric. Despite these advancements, the model still grapples with a significant limitation: a growing disconnect between academic research and its practical applications, primarily attributed to dataset over-fitting. For future endeavours, there should be an establishment of a robustness and deployment fitness score, emphasizing comprehensive code reviews and real-world simulation testing. What is more, using generative models such as robGAN to assess a model's readiness for deployment should be considered. The deep learning network TrajNet++ by [PERSON] et al. [63] effectively simulates group interactions within high-density settings, thereby facilitating social navigation for robots. The model is able to learn a variety of social norms used by humans when walking in large groups. The proposed metrics show that the TrajNet++ model outperforms traditional domain knowledge-based methods for modelling group interactions in the ORCA synthetic dataset [99]. The innovativeness of the TrajNet++ model is its excellence in simulating group interactions in high-density settings, which enables robots to navigate socially. It adeptly learns diverse human social norms, especially in large group dynamics. Moreover, it outperforms traditional methods, as evidenced by its performance on the ORCA synthetic dataset. However, its reliance on this synthetic dataset raises questions about its adaptability in real-world scenarios. For future exploration, there is potential in deploying TrajNet++ in real-world applications such as intelligent transportation systems and refining its capabilities to capture a broader range of social interactions. In the study by [PERSON] et al. [64], a model is introduced that includes two main parts: a cross-attention module and a transformer block. This model is effective in predicting human trajectories based on certain conditions, which is illustrated by its use of iterative attention blocks. The cross-attention module maps a latent array and free-form input information to a latent array, while the transformer block maps a latent array to a latent array. These components are used alternately to process the input information. The model uses multiple layers of cross-attention to iteratively extract information from the input data. The encoder consists of a stack of N identical blocks, with each block having three cross-attention modules and a latent transformer. The attention module is permutationally invariant, which is not suitable for using temporal information. To preserve temporal information, 1D positional encodings are concatenated with agent embedding. The latent transform block uses the GPT-2 architecture, which is itself a decoder of the original transform architecture. Cross-attention is an attention layer that decomposes attention into multiple heads. The trajectory generation problem is divided into two stages: proposing the position of the pedestrian target and constructing trajectories conditioned on the proposed target position. The target decoder decodes the final endpoint position from the encoded latent array using the multilayer perceptron, while the trajectory decoder decodes the trajectory using targets true during training and targets predicted during inference.