# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Probabilistic Methods  
In this section, a series of articles were provided. These articles shed light on the model's non-deterministic prediction of human trajectory and underscore its distinction from a neural network. In their research dedicated to human trajectory prediction, [PERSON] et al. [71] propose the implementation of a Hidden Markov Model (HMM), utilizing the Viterbi algorithm. The warehouse on which the algorithm would operate uses collecting robots that are supposed to stop moving when they detect a possible collision with a human. The ADE error they validated their solution with is the largest in the last step by far. As a baseline for ADE, they cite path interpolation, which firstly is not correct due to impossible paths, and also achieves worse ADE results. Hence, they were able to determine that the model performs better than interpolation. The innovations that researchers introduced is the utilization of a Hidden Markov Model combined with the Viterbi algorithm. This enables estimation of worker intentions by analysing both observed and hypothesized motions. This offers a more nuanced understanding of worker behaviour. However, the research had its limitations. The baseline path interpolation method used for average displacement error (ADE) validation was flawed, producing impossible paths. Moreover, the ADE error was notably large in the final prediction step, highlighting potential issues in long-term forecasting. For future endeavours, refining the model for better long-term predictions, real-world testing, and expanding the framework to handle more intricate warehouse scenarios are logical next steps to enhance prediction accuracy and overall system efficiency. The article by [PERSON] et al. [72] discusses the challenges of predicting human movement trajectories in manufacturing. Furthermore, the authors present a schedule-based \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{5}{c}{**Paper**} \\ \cline{3-6}  & & [61] & [64] & [67] & [68] & [69] \\ \hline \multirow{2}{*}{**ETH**} & **ADE (m)** & 0.54 & 0.62 & 0.28 & 0.71 & 0.25 \\ \cline{2-6}  & **FDE (m)** & 0.87 & 1.13 & 0.33 & 1.66 & 0.24 \\ \hline \multirow{2}{*}{**ETH/UCY Average**} & **ADE (m)** & 0.29 & 0.52 & 0.18 & 0.37 & 0.17 \\ \cline{2-6}  & **FDE (m)** & 0.48 & 0.75 & 0.27 & 0.91 & 0.24 \\ \hline \multirow{2}{*}{**SSD (k = 20)**} & **ADE (px)** & 9.96 & - & 7.85 & - & 6.52 \\ \cline{2-6}  & **FDE (px)** & 15.88 & - & 11.85 & - & 10.61 \\ \hline \multirow{2}{*}{**SSD (k = 5)**} & **ADE (px)** & 12.79 & - & 11.49 & - & - \\ \cline{2-6}  & **FDE (px)** & 29.58 & - & 20.23 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of quantitative results of models evaluated on ETH/UCY or SSD datasets. \begin{table}
\begin{tabular}{l l} \hline \hline
**Evaluation Method** & **Papers** \\ \hline Simulation & [PERSON] et al. [59], [PERSON] et al. [60] \\ \hline \multirow{2}{*}{Benchmark} & [PERSON] et al. [61], [PERSON] et al. [64], [PERSON] et al. [65], \\  & [PERSON] et al. [67], [PERSON] et al. [68], [PERSON] et al. [69] \\ \hline \multicolumn{2}{l}{Synthetic datasets} & [PERSON] and [PERSON] [62], [PERSON] et al. [63] \\ \hline \multicolumn{2}{l}{Real-world case} & [PERSON] et al. [66], [PERSON] et al. [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of evaluation methods used in the papers on deep learning methods. approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). The application of scheduling in human motion trajectory prediction enhances semantic mapping approaches. This approach effectively reduces the number of possible destinations by considering the subsequent process steps for the goods currently in production. By reducing the set of destinations, the performance of future trajectory prediction can be improved. For evaluation, a commercial MES system is used together with an Ultra Wideband (UWB)-based real-time localization system to obtain human position data.