Award Date

5-1-2025

Degree Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Electrical and Computer Engineering

First Committee Member

Brendan Morris

Second Committee Member

Venkatesan Muthukumar

Third Committee Member

Emma Regentova

Fourth Committee Member

Shashi Nambisan

Number of Pages

76

Abstract

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This dataset captures diverse traffic scenarios with annotated trajectories, addressing a gap in existing resources by providing realistic, campus-specific interactions for validation. The proposed model combines GAT with Temporal Convolutional Networks (TCN) and a sequence-to-sequence framework, enabling adaptive weighting of agent interactions to enhance prediction accuracy. Experimental results demonstrate the model’s effectiveness across different environments. On the ApolloScape benchmark, the model achieves a 5.98% reduction in Average Displacement Error (ADE) and a 6.76% reduction in Final Displacement Error (FDE) compared to baseline method. Evaluations on the UNLVTraj dataset further validate its robustness in predicting trajectories for heterogeneous agents, even in unstructured, map-free settings. By offering a scalable, cost-effective solution and a publicly available dataset, this work supports future research in map-free navigation.

Keywords

autonomous vehicles; deep learning; machine learning; self-driving

Disciplines

Computer Engineering | Electrical and Computer Engineering | Library and Information Science

File Format

pdf

File Size

7200 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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