Pedestrian Behavior Prediction Using Machine Learning Methods

Sammanfattning:

Pedestrian safety is a critical global concern, with an urgent need to reduce fatalities caused by pedestrian-vehicle collisions. Machine learning offers a powerful tool for reducing these collisions by improving the prediction of pedestrian behavior. This thesis focuses on predicting and analyzing pedestrian behavior in complex traffic scenarios using machine learning methods.

After reviewing existing studies, we propose deep learning models to improve pedestrian trajectory prediction. These models consider social interactions between pedestrians and their interactions with vehicles, enhancing both accuracy and inference speed. Additionally, we improve model transferability by including spectral features.

We also investigate pedestrian crossing intentions when interacting with vehicles using machine learning methods. Key factors such as the presence of zebra crossings, waiting time, walking speed, and missed crossing chances strongly influence pedestrian behavior. A cross-country comparison between Japan and Germany reveals both similarities and differences in pedestrian behavior, providing valuable insights into model transferability.

Overall, this research advances the prediction of pedestrian behavior, providing insights for safer pedestrian-vehicle interactions in complex scenarios. These findings can guide the development of smarter, safer automated driving systems.

Fakultetsopponent:

Associate professor He Wang
Department of Computer Science, University College London

Betygsnämnd:

Professor Maria Riveiro, Jönköping University
Professor Andreas Vogelsang, University of Cologne
Professor Markus Lienkamp, Technical University of Munich

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