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Machine Learning Solutions for the Task of Pedestrian Trajectory Prediction – A Systematic Literature Review
(Tartu Ülikool, 2024) Garg, Ankit; Muhammad, Naveed, juhendaja; Zabolotnii, Dmytro, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
This study aims to provide an in-depth overview of existing methodologies, trends, and challenges in human trajectory prediction. It analyzes diverse literature to examine various approaches involved in machine learning techniques. This study categorizes these methodologies based on their foundational principles, delving into their strengths and limitations. Particular emphasis is placed on recent advances in machine learning mixed with psychological and environmental aspects for human trajectory prediction. This study finds three significant categories: cognitive approaches, pattern-based approaches, and probabilistic approaches. These are then further divided into different sub-categories, thus forming a taxonomy. Categories at each level of the hierarchical taxonomy are compared, with information about their pros, cons, and where each category should be used. Furthermore, the research papers studied during this survey were split into categories based on their methods. In conclusion, it was found that the “Behavioral Features Method” category performed the best among the other categories. Thus, more research should be done on combining machine learning methods with behavioral features.