University of Illinois Urbana-Champaign

An end-to-end agent-map interaction framework for multi-agent trajectory prediction

Li, Xiang

Content Files
LI-THESIS-2023.pdf

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https://hdl.handle.net/2142/120114

Description

Title
An end-to-end agent-map interaction framework for multi-agent trajectory prediction
Author(s)
Li, Xiang
Issue Date
2023-04-27
Director of Research (if dissertation) or Advisor (if thesis)
Driggs-Campbell, Katie
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Autonomous driving
  • Machine learning
  • Trajectory prediction
  • Artificial intelligence.
Abstract
This thesis proposes an end-to-end agent-map interaction framework for multi-agent trajectory prediction, which can be used in motion prediction tasks in relatively complex traffic scenarios that involve multiple agents, such as vehicles and pedestrians. The framework consists of an end-to-end deep learning model which takes motion history information of agents and rasterized context map image as input and predicts the future trajectories of all agents and occupancy on the map. The agents and map input features are extracted by capturing the interactions among agents, the temporal relationship of agents' motion, and the spatial information contained in the map. Agents and map embeddings interact with each other via a symmetric transformer structure based on cross-attention, then predictions of trajectories and occupancy are generated from post-interaction embeddings. The proposed framework does not utilize any extra internal levels of input representations such as lane graphs, motion heat maps, and waypoints; thus it is easier to be generalized for inputs with different modalities. The framework is implemented and then trained and evaluated on the nuScenes dataset. The framework is compared with many state-of-the-art works
Graduation Semester
2023-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/120114
Copyright and License Information
Copyright 2023 Xiang Li

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois