Theses Doctoral

Physics-Informed Deep Learning for Trajectory Prediction and Uncertainty Quantification

Mo, Zhaobin

Trajectory prediction aims to forecast future trajectories of agents (such as vehicles, pedestrians) based on their historical values. It is a fundamental step for advancing transportation management and control, directly impacting the safety and efficiency of modern transportation systems. In this research domain, deep learning-based methods have been widely adopted, achieving impressive performance. However, these methods have several drawbacks. First, they require substantial amounts of data. Second, they are prone to randomness inherent in real-world data. Third, complex interactions among transportation agents impose high demands on deep learning models.

This dissertation seeks to address these challenges through physics-informed deep learning (PIDL), a promising approach that integrates physics-based prior knowledge into data-driven models. The dissertation is organized into three parts, focusing on different aspects of applying PIDL for trajectory prediction. First, we formulate the problem of single-agent trajectory prediction using PIDL. Second, we enhance PIDL by incorporating uncertainty quantification, accounting for uncertainties in both data and model parameters, and predicting future trajectories with confidence intervals. Third, we extend the single-agent trajectory prediction problem to a multi-agent setting, employing graph neural networks to model complex spatial interactions and NeuralODE to capture long-term dependencies.

Through evaluations on both numerical and real-world datasets, our proposed methods demonstrate improved performance compared to state-of-the-art approaches. Moreover, leveraging physics-based prior knowledge makes our methods particularly robust in scenarios where deep learning models struggle, such as data-scarce environments and long-term predictions.

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More About This Work

Academic Units
Civil Engineering and Engineering Mechanics
Thesis Advisors
Di, Xuan Sharon
Degree
Ph.D., Columbia University
Published Here
January 15, 2025