Citation

Yogarayan, Sumendra and Abdul Razak, Siti Fatimah and Mogan, Jashila Nair and Azman, Afizan and Sivaprakasam, Avenaish (2025) Alertness Analytics. In: The Smart Life Revolution. CRC Press, pp. 21-44. ISBN 978-104036402-4, 978-103283405-4

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Abstract

In the context of vehicular safety, the advent of deep learning has led to transformative advancements, particularly in the domain of driver drowsiness and alcohol impairment detection. This chapter explores the application of deep learning models that serve as pivotal elements in the development of intelligent systems aimed at reducing road accidents. A deep learning model functions as an analytical tool, continuously evolving through the incorporation of different data streams such as facial recognition patterns, eye movement metrics and behavioural indicators. This dynamic combination of data enables the model to accurately determine signs of fatigue and intoxication in drivers. The potential of these AI-driven models lies in their capacity to learn from huge amounts of data, identify correlations, and predict safety hazards before they become perceptible. By leveraging deep learning algorithms, these systems empower automotive safety engineers to formulate preventive strategies, enhance driver monitoring protocols and optimise response mechanisms. The result of these efforts is a significant improvement in road safety, a reduction in accident rates and the advancement of a more secure safe driving environment. This chapter presents an exploration of the capabilities, advantages and challenges associated with the implementation of deep learning models for driver drowsiness and alcohol detection. It features the key role of AI in reinforcing vehicular safety and the profound impact it holds for the future of transportation.

Item Type:	Book Section
Uncontrolled Keywords:	Deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	26 Jun 2025 07:04
Last Modified:	26 Jun 2025 07:04
URII:	http://shdl.mmu.edu.my/id/eprint/14100

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