基于智能手机数据统计特征的神经网络驾驶员识别
Driver Identification by Neural Network on Extracted Statistical Features from Smartphone Data
摘要：
交通的未来是由使用人工智能来改善生活和交通所驱动的。本文提出了一种基于神经网络的系统，用于使用智能手机收集的数据进行驾驶员身份识别。该系统可以自动、可靠和实时地识别驾驶员，无需面部识别，也不会侵犯隐私。该系统由数据汇集、预处理和识别三个模块组成。在数据汇集模块中，使用智能手机收集加速度计和陀螺仪传感器的数据。预处理模块包括噪声去除、数据清洗和分割。在该模块中，将恢复丢失的值，并删除停止车辆的数据。最后，从数据窗口中提取有效的统计特性。在识别模块中，使用机器学习算法来识别驾驶员的模式。实验表明，最佳的驾驶员身份识别算法是MLP，最高准确率为96%。该解决方案可用于未来的交通运输，以开发基于驾驶员的保险系统，以及开发用于实施惩罚和激励的系统。
英文摘要：
The future of transportation is driven by the use of artificial intelligence to improve living and transportation. This paper presents a neural network-based system for driver identification using data collected by a smartphone. This system identifies the driver automatically, reliably and in real-time without the need for facial recognition and also does not violate privacy. The system architecture consists of three modules data collection, preprocessing and identification. In the data collection module, the data of the accelerometer and gyroscope sensors are collected using a smartphone. The preprocessing module includes noise removal, data cleaning, and segmentation. In this module, lost values will be retrieved and data of stopped vehicle will be deleted. Finally, effective statistical properties are extracted from data-windows. In the identification module, machine learning algorithms are used to identify drivers' patterns. According to experiments, the best algorithm for driver identification is MLP with a maximum accuracy of 96%. This solution can be used in future transportation to develop driver-based insurance systems as well as the development of systems used to apply penalties and incentives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
