A comprehensive review of embedded systems in autonomous vehicles: Trends, challenges, and future directions
S Sonko, EA Etukudoh, KI Ibekwe, VI Ilojianya… - World Journal of …, 2024 - wjarr.com
The integration of embedded systems in autonomous vehicles represents a transformative
paradigm shift in the automotive industry, offering unprecedented opportunities for …
paradigm shift in the automotive industry, offering unprecedented opportunities for …
Deep learning and autonomous vehicles: Strategic themes, applications, and research agenda using SciMAT and content-centric analysis, a systematic review
FE Morooka, AM Junior, TFAC Sigahi, JS Pinto… - Machine Learning and …, 2023 - mdpi.com
Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained
increasing interest from both researchers and companies. This has caused a rapid …
increasing interest from both researchers and companies. This has caused a rapid …
Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential
Automated machine learning (AutoML) is a generic term for a specific approach to machine
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach
Traffic safety is a critical factor that has always been considered in policy making for urban
transportation planning and management. Accurately predicting crash risk hot spots allows …
transportation planning and management. Accurately predicting crash risk hot spots allows …
An empirical review of automated machine learning
L Vaccaro, G Sansonetti, A Micarelli - Computers, 2021 - mdpi.com
In recent years, Automated Machine Learning (AutoML) has become increasingly important
in Computer Science due to the valuable potential it offers. This is testified by the high …
in Computer Science due to the valuable potential it offers. This is testified by the high …
An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk prediction
In recent years, machine learning (ML)-based approaches have gained increasing attention
in occupational accident research. However, the challenges of data uncertainty …
in occupational accident research. However, the challenges of data uncertainty …
Cybersecurity of autonomous vehicles: A systematic literature review of adversarial attacks and defense models
Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …
development and improvement of deep learning (DL) technology. However, the uptake of …
A lane-changing risk profile analysis method based on time-series clustering
Lane-changing (LC) is an essential driving maneuver on roadways, and risky LC maneuvers
account for a large number of crash accidents. This study investigates the LC risk profile …
account for a large number of crash accidents. This study investigates the LC risk profile …
A data-driven feature learning approach based on Copula-Bayesian Network and its application in comparative investigation on risky lane-changing and car-following …
Abstract The era of 'Big Data'provides opportunities for researchers to have deep insights
into traffic safety. By taking advantages of 'Big Data', this study proposes a data-driven …
into traffic safety. By taking advantages of 'Big Data', this study proposes a data-driven …
Autonomous vehicular overtaking maneuver: A survey and taxonomy
Autonomous vehicles (AVs) are the next-generation driver-less vehicular entities with
advanced technologies. Overtaking is an important and challenging maneuver that needs to …
advanced technologies. Overtaking is an important and challenging maneuver that needs to …