利用指数平滑提高学习向量量化(LVQ)的驾驶行为分类准确率

P. D. Setyorini, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti
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引用次数: 1

摘要

驾驶行为的识别对于发现异常驾驶情况(如事故)非常重要。在这项工作中,使用LVQ(学习向量量化)算法来识别驾驶活动。该算法创建了一个原型,易于为每个应用领域的专家解释。每个驾驶活动的数据集由加速度计传感器和android智能陀螺仪获得。对传感器数据集采用指数平滑方法,提高分类结果的准确性。对陀螺仪传感器数据集进行平滑后的分类,精度达到90.429%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy Improvement on Learning Vector Quantization (LVQ) Using Exponential Smoothing for Driving Activity Classification
Identification of driving activities is important to find out abnormal driving conditions such as accidents. In this work, identifying driving activities is carried out using the LVQ (Learning Vector Quantization) algorithm. This algorithm creates a prototype that is easily interpreted for experts in each application domain. The dataset for each driving activity is obtained from the accelerometer sensor and the android smart gyroscope. The exponential smoothing method is used in the sensor dataset to improve the accuracy of classification results. The best accuracy is obtained from the classification of the gyroscope sensor dataset after smoothing with an accuracy of 90.429%.
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