A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Hussain, K; Moreira, C; Pereira, J; Jardim, S; Jorge, J

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Hussain, K Moreira, C Pereira, J Jardim, S Jorge, J

Permalink

Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Smart Cities, 2025, 8, (3)
Issue Date:: 2025-06-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.23 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Hussain, K
dc.contributor.author	Moreira, C
dc.contributor.author	Pereira, J
dc.contributor.author	Jardim, S
dc.contributor.author	Jorge, J
dc.date.accessioned	2025-12-16T02:56:43Z
dc.date.available	2025-12-16T02:56:43Z
dc.date.issued	2025-06-01
dc.identifier.citation	Smart Cities, 2025, 8, (3)
dc.identifier.issn	2624-6511
dc.identifier.issn	2624-6511
dc.identifier.uri	http://hdl.handle.net/10453/190949
dc.description.abstract	Highlights: What are the main accomplishments of this study? A comprehensive analysis of deep learning techniques in both on-road and autonomousracing cars, highlighting distinct challenges and requirements for each context. The identification of critical challenges for future research, to ensure safety and performancein autonomous systems. What are the implications of the main findings? The detailed evaluation of planning methods and performance metrics points to opportunitiesto refine existing methodologies and identify emerging research areasthat can guide the development of more efficient, robust, and scalable autonomousdriving technologies. The challenges identified in sensor fusion, environmental robustness, and computationalefficiency imply that addressing these issues is critical to progress inautonomous systems. Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, simulations, and real-time platforms. Additionally, we compare various evaluation metrics and performance outcomes for current methodologies. Finally, we offer insights into emerging research directions based on the latest advancements, providing a roadmap for future innovation in autonomous driving.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Smart Cities
dc.relation.isbasedon	10.3390/smartcities8030079
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-12-16T02:56:41Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	3

Abstract:

Highlights: What are the main accomplishments of this study? A comprehensive analysis of deep learning techniques in both on-road and autonomousracing cars, highlighting distinct challenges and requirements for each context. The identification of critical challenges for future research, to ensure safety and performancein autonomous systems. What are the implications of the main findings? The detailed evaluation of planning methods and performance metrics points to opportunitiesto refine existing methodologies and identify emerging research areasthat can guide the development of more efficient, robust, and scalable autonomousdriving technologies. The challenges identified in sensor fusion, environmental robustness, and computationalefficiency imply that addressing these issues is critical to progress inautonomous systems. Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, simulations, and real-time platforms. Additionally, we compare various evaluation metrics and performance outcomes for current methodologies. Finally, we offer insights into emerging research directions based on the latest advancements, providing a roadmap for future innovation in autonomous driving.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/190949