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In crowd scenarios, predicting trajectories of pedestrians is a complex andchallenging task depending on many external factors. The topology of the sceneand the interactions between the pedestrians are just some of them. Due toadvancements in data-science and data collection technologies deep learningmethods have recently become a research hotspot in numerous domains. Therefore,it is not surprising that more and more researchers apply these methods topredict trajectories of pedestrians. This paper compares these relatively newdeep learning algorithms with classical knowledge-based models that are widelyused to simulate pedestrian dynamics. It provides a comprehensive literaturereview of both approaches, explores technical and application orienteddifferences, and addresses open questions as well as future developmentdirections. Our investigations point out that the pertinence of knowledge-basedmodels to predict local trajectories is nowadays questionable because of thehigh accuracy of the deep learning algorithms. Nevertheless, the ability ofdeep-learning algorithms for large-scale simulation and the description ofcollective dynamics remains to be demonstrated. Furthermore, the comparisonshows that the combination of both approaches (the hybrid approach) seems to bepromising to overcome disadvantages like the missing explainability of the deeplearning approach.

Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches