Review on Neural Network based Detection System for Intoxicated Driving

Anupama Hari, Joshua Thomas
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Abstract

Driving situations include a lot of safety considerations. One of the major factors in road accidents is drunk driving by drivers of vehicles. Due to advancements, notably in the area of biometrics, it is now possible to determine a person’s state of drunkenness on alcohol. Therefore, creating an intelligent system to address this issue is important. These systems generally employ artificial vision or sensor networks. For the purpose of evaluating the collected data, we additionally utilize feature selection and supervised classification methods. Every technique and algorithm has been created to use the least amount of analytical resources feasible because the whole acquisition and analysis process will be completed within an embedded device. This paper provides an overview of current developments in the field of neural network-based driver intoxication detection. Different methods for detecting drunkenness have been examined, and potential areas for improvement have been identified.
基于神经网络的醉酒驾驶检测系统研究进展
驾驶情况包括很多安全方面的考虑。道路交通事故的一个主要因素是司机酒后驾驶。由于生物计量学领域的进步,现在可以通过酒精判断一个人的醉酒状态。因此,创建一个智能系统来解决这个问题是很重要的。这些系统通常采用人工视觉或传感器网络。为了评估收集到的数据,我们还使用了特征选择和监督分类方法。每一种技术和算法都是为了使用最少的分析资源而创建的,因为整个采集和分析过程将在嵌入式设备中完成。本文概述了基于神经网络的驾驶员醉酒检测领域的最新进展。检测醉酒的不同方法已经进行了研究,并确定了潜在的改进领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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