Research Article An Optimized Approach Using Transfer Learning to Detect Drunk Driving Ankit Kumar , 1 Ajay Kumar , 1 Mayank Singh , 1 Pradeep Kumar , 1 and Anchit Bijalwan 2 1 Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida, India 2 Faculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia Correspondence should be addressed to Anchit Bijalwan; anchit.bijalwan@amu.edu.et Received 21 July 2022; Accepted 5 September 2022; Published 20 September 2022 Academic Editor: Punit Gupta Copyright © 2022 Ankit Kumar et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Although the statistics show a slow decline in traffic accidents in many countries over the last few years, drunk or drug-influenced driving still contributes to enough shares in those records to act. Nowadays, breath analysers are used to estimate breath alcohol content (BAC) by law enforcement as a preliminary alcohol screening in many countries. erefore, since breath analysers or field sobriety testers do not accurately measure BAC, the analysis of blood samples of individuals is required for further action. Many researchers have presented various approaches to detect drunk driving, for example, using sensors, face recognition, and a driver’s behaviour to confound the shortcomings of the time-honoured approach using breath analysers. But each one has some limitations. is study proposed a plan to distinguish between drivers’ states, that is, sober or drunk, by the use of transfer learning from the convolutional neural network (CNN) features to the random forest (RF) features with an accuracy of up to 93%, which is higher than that of existing models. With the same dataset, to validate our research, a comparative analysis was performed with other existing model classifiers such as the simple vector machine (SVM) with an accuracy of 65% and the K-nearest neighbour (KNN) with an accuracy of 62%, and it was found that our approach is an optimized approach in terms of accuracy, precision, recall, F1-score, AUC-ROC curve, and Matthew’s correlation coefficient (MCC) with confusion matrix. 1. Introduction As per the Ministry of Road Transport and Highways, India, traffic collisions result in the casualties of approximately four lakh individuals and leave nearly fifty thousand individuals with nonfatal injuries around the country each year [1]. In simple words, if the crowd is going to watch a match in a stadium with a capacity of one lakh and among them, a road crash happens while approaching the stadium, there is a chance that at least one person will die and up to thirty people will hold nonfatal injuries. e victims of such ac- cidents and lethal and nondeadly wounds include weak street clients, such as pedestrians, cyclists, motorcyclists, and other travellers. Other than the citizens’ existence lost in misfortunes, they also cause a heavy monetary weight on their families, such as treatment and last rites costs. Likewise, automobile accidents sway public economies, costing countries practically two percent of their yearly growth in domestic production. A driver affected by intoxicants has a critical danger factor for a conveyance accident. Drunk driving dramatically increases road traffic injury risk as the driver’s blood alcohol concentration increases. Drunk driving increases road traffic injury risk to varying degrees depending on psychoactive drug abuse. Many solutions are available in the industry to prevent traffic collisions. However, either they are expensive, like autopilot cars, or nonscalable and difficult to implement, such as law en- forcement personnel using a breath analyser to check alcohol in the air drivers breathe out [2]. Nevertheless, the world is a bystander to severe traffic collisions and casualties. Let us drive into a considerable effort made by researchers to curb traffic accidents due to drunk driving that happened in the last two decades, inspired by modern technology of their age in a category manner. e problem statement is to find an efficient system to detect drunk driving that gives accurate results in order to Hindawi Scientific Programming Volume 2022, Article ID 8775607, 12 pages https://doi.org/10.1155/2022/8775607