Predicting Agent Behavior by Estimating Motion Planners

Loading...
Thumbnail Image

Authors

Poston, Jamie Eileen

Issue Date

2020

Type

Thesis

Language

Keywords

autonomous , machine learning , neural network , path planner , path prediction , robotics

Research Projects

Organizational Units

Journal Issue

Alternative Title

Abstract

To navigate the world safely, autonomous agents must predict the future actions of the other agents in the world. We propose a method to estimate the future positions of other agents by using a sample-based planner that samples from a distribution trained on observed vehicle trajectories. The sample-based planner used is Rapidly-exploring Random Tree, and trained distribution is the combination of two networks: a Long Short-Term Memory network and a Mixture Density Network. We compared this trained model to several baselines. The proposed method performs better than the curve fitting and Long Short-Term Memory baselines, but worse than the other two baselines. The methods with a path planner perform better than the baselines without, which may mean that using a path planner to complete the problem of vehicle trajectory prediction may prove beneficial in future works.

Description

Citation

Publisher

License

Journal

Volume

Issue

PubMed ID

DOI

ISSN

EISSN