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

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

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Li, Xiang

Contributors dc:contributor

• Driggs-Campbell, Katie

• Autonomous Driving
• Machine Learning
• Trajectory Prediction
• Artificial Intelligence.

Statement dc:rights

• Copyright 2023 Xiang Li

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120114