CS Events

Qualifying Exam Enhanced Multi-Agent Trajectory Forecasting Using Ordinary Differential Equations
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Speaker: Song Wen

Location : CoRE 301

Committee: 

Professor Dimitris Metaxas (Chair)

Professor Hao Wang

Professor Konstantinos Michmizos

Professor Dong Deng

Event Type: Qualifying Exam

Abstract:  Multi-agent trajectory forecasting aims to estimate future agent trajectories given the historical trajectories of multiple agents. It has recently attracted a lot of attention due to its widespread applications including autonomous driving, physical system modeling and urban data mining. It is challenging because both complex temporal dynamics and agent interaction jointly affect each agent. Existing methods often fall short in capturing these two factors explicitly, because they neglect the continuous nature of the system and distance information between agents, which leads to limited forecasting accuracy and poor interpretability. Innovatively, Neural Ordinary Differential Equations (ODEs) introduce a novel paradigm of continuous-time neural networks by solving ODEs. In this talk, I will review my works that utilize ODEs to enhance multi-agent trajectory forecasting by incorporating distance information and explicitly modeling underlying continuous temporal dynamics. Our experiments demonstrate that our works not only improve the trajectory forecasting accuracy, but also adeptly deal with unexpected events which are not in the training dataset.

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Contact  Professor Dimitris Metaxas (Chair)