End-to-end autonomous driving based on machine learning

In this project, we will explore the end-to-end approach to autonomous driving through machine learning. This project will be executed on the Donkey Simulator, a remote-control car simulator built on the Unity game platform. We will focus mainly on reinforcement learning algorithms as our primary ma...

Full description

Saved in:
Bibliographic Details
Main Author: Teo, Ren Hao
Other Authors: Lyu Chen
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167648
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-167648
record_format dspace
spelling sg-ntu-dr.10356-1676482023-06-03T16:50:44Z End-to-end autonomous driving based on machine learning Teo, Ren Hao Lyu Chen School of Mechanical and Aerospace Engineering lyuchen@ntu.edu.sg Engineering::Mechanical engineering In this project, we will explore the end-to-end approach to autonomous driving through machine learning. This project will be executed on the Donkey Simulator, a remote-control car simulator built on the Unity game platform. We will focus mainly on reinforcement learning algorithms as our primary machine learning method. This report seeks to run simulations on autonomous driving based on selected on-policy and off-policy algorithms. These methods are namely, Proximal Policy Optimization (PPO) for the on-policy method and Soft Actor-Critic (SAC) as our off-policy method. In addition, we will be exploring the use of entropy regularization by passing different entropy coefficients while training our models to explore its effects on training. Rewards to the model are calculated based on the speed of the agent and the ability to stay in the centre of the track. The results from the trained models produced by these algorithms will then be drawn into comparison, based on the cumulative rewards of each algorithm. Bachelor of Engineering (Mechanical Engineering) 2023-05-30T08:49:28Z 2023-05-30T08:49:28Z 2023 Final Year Project (FYP) Teo, R. H. (2023). End-to-end autonomous driving based on machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167648 https://hdl.handle.net/10356/167648 en C076 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Teo, Ren Hao
End-to-end autonomous driving based on machine learning
description In this project, we will explore the end-to-end approach to autonomous driving through machine learning. This project will be executed on the Donkey Simulator, a remote-control car simulator built on the Unity game platform. We will focus mainly on reinforcement learning algorithms as our primary machine learning method. This report seeks to run simulations on autonomous driving based on selected on-policy and off-policy algorithms. These methods are namely, Proximal Policy Optimization (PPO) for the on-policy method and Soft Actor-Critic (SAC) as our off-policy method. In addition, we will be exploring the use of entropy regularization by passing different entropy coefficients while training our models to explore its effects on training. Rewards to the model are calculated based on the speed of the agent and the ability to stay in the centre of the track. The results from the trained models produced by these algorithms will then be drawn into comparison, based on the cumulative rewards of each algorithm.
author2 Lyu Chen
author_facet Lyu Chen
Teo, Ren Hao
format Final Year Project
author Teo, Ren Hao
author_sort Teo, Ren Hao
title End-to-end autonomous driving based on machine learning
title_short End-to-end autonomous driving based on machine learning
title_full End-to-end autonomous driving based on machine learning
title_fullStr End-to-end autonomous driving based on machine learning
title_full_unstemmed End-to-end autonomous driving based on machine learning
title_sort end-to-end autonomous driving based on machine learning
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167648
_version_ 1772825992247640064