基于卷积KNN的违规驾驶行为诊断

Chandramma, M. R
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引用次数: 0

摘要

对不寻常的攻击性驾驶行为进行实际观察对提高汽车安全至关重要。加强骑乘的习惯和行为,以避免致命的撞车事故。倾向于感知异常驾驶事物的利用越来越普遍,因为这对目前无人驾驶汽车的水平以及车内乘客和驾驶员的健康都至关重要。深度学习技术的最新发展,如现代深度神经网络令人印象深刻的扩展能力,以及完全重新训练数据驱动模型所需的大量电影剪辑,可能会极大地帮助解决这个具有挑战性的问题。最后,提出了新的大规模持续学习模型。这些方法的动机是最近创建和广泛使用的网络cnn模型被称为过度驱动控制系统(ADBD网)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing Adbdconvolutional KNN, Diagnosis of Irregular Driver Behavior
Actual observation of unusual aggressive driving is essential for enhancing car safety. to enhance riding habits and behaviour in order to keep fatal crashes. The utilization of perception abnormal driving things that tend is becoming more and more common since it is essential to both the current level of driverless vehicles and the health of both passengers and motorists in vehicles. Recent developments in deep learning techniques, such as the impressive extension capacity of modern deep neural networks and the vast quantities of film clips necessary for fully retraining the data-driven models, may substantially help with this challenging problem. To conclude the study, new massive continuing to learn models are provided. These methods are motivated by the recently created and extensively used networked cnn model known as the Excessive Drive Control System in place (ADBD Net).
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