Authors : Om Unde; Pranali Vhora

Volume/Issue : Volume 9 - 2024, Issue 11 - November

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Google Scholar : https://tinyurl.com/mta37n27

Scribd : https://tinyurl.com/yd3mfdv4

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV664

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The increase in intelligent transport systems has stimulated a rising curiosity in applying deep learning to identify distracted and hostile driving actions. This type of conduct continues to be a major factor in car crashes, resulting in significant social and economic consequences. This article examines recent progress in utilizing deep learning methods for identifying distracted and hostile driving behaviors. Moreover, it emphasizes the need for certain technical and environmental prerequisites for successful execution, such as acquiring data, hardware, and software specifications. In conclusion, we investigate the unresolved issues like issues related to data privacy, the ability to interpret deep learning models, and differences in driver behaviors.

Keywords : Detection of Driver Behavior Using Deep Learning in Intelligent Transport Systems, Including Identification of Inattentive and Aggressive Driving.

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