@Article{crph944,
AUTHOR = {Mandala, Vishwanadham and Kuppala, Bala Maruthi Subba Rao and Surabhi, Srinivas Naveen Reddy Dolu and Kommisetty, Phani Durga Nanda Kishore},
TITLE = {Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {9-20},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/944},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.944},
ABSTRACT = {The recent evidence on AI in automotive safety shows the potential to reduce crashes and improve efficiency. Studies used AI techniques like machine learning and predictive analytics models to develop predictive collision avoidance systems. The studies collected data from various sources, such as traffic collision data and shapefiles. They utilized deep learning neural networks and 3D visualization techniques to analyze the data. However, there needs to be more research on AI in school bus and commercial truck safety. This paper explores the importance of AI-driven predictive failure analytics in enhancing automotive safety for these vehicles. It will discuss challenges, required data, technologies involved in predictive failure analytics, and the potential benefits and implications for the future. The conclusion will summarize the findings and emphasize the significance of AI in improving driver safety. Overall, this paper contributes to the field of automotive safety and aims to attract more research in this area.},
}

%0 Journal Article
%A Mandala, Vishwanadham
%A Kuppala, Bala Maruthi Subba Rao
%A Surabhi, Srinivas Naveen Reddy Dolu
%A Kommisetty, Phani Durga Nanda Kishore
%D 2021
%J Current Research in Public Health

%@ 2831-5162
%V 1
%N 1
%P 9-20

%T Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks
%M doi:10.31586/jaibd.2022.944
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/944

TY  - JOUR
AU  - Mandala, Vishwanadham
AU  - Kuppala, Bala Maruthi Subba Rao
AU  - Surabhi, Srinivas Naveen Reddy Dolu
AU  - Kommisetty, Phani Durga Nanda Kishore
TI  - Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks
T2  - Current Research in Public Health
PY  - 2021
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 9
EP  - 20
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/944
AB  - The recent evidence on AI in automotive safety shows the potential to reduce crashes and improve efficiency. Studies used AI techniques like machine learning and predictive analytics models to develop predictive collision avoidance systems. The studies collected data from various sources, such as traffic collision data and shapefiles. They utilized deep learning neural networks and 3D visualization techniques to analyze the data. However, there needs to be more research on AI in school bus and commercial truck safety. This paper explores the importance of AI-driven predictive failure analytics in enhancing automotive safety for these vehicles. It will discuss challenges, required data, technologies involved in predictive failure analytics, and the potential benefits and implications for the future. The conclusion will summarize the findings and emphasize the significance of AI in improving driver safety. Overall, this paper contributes to the field of automotive safety and aims to attract more research in this area.
DO  - Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks
TI  - 10.31586/jaibd.2022.944
ER  -