标题： 机动识别 -- 用于车辆遥测领域时间序列分类的 Python 包 显示英文标题

标题： maneuverRecognition -- A Python package for Timeseries Classification in the domain of Vehicle Telematics

摘要： 在车辆远程信息处理领域，自动识别驾驶操作用于对驾驶行为进行分类和评估。 这不仅作为增强保险政策个性化的一个组成部分，而且有助于提高道路安全性，减少事故及其相关成本，以及减少燃油消耗并支持环保驾驶。 在此背景下，操作识别在技术上需要连续应用时间序列分类，这对远程信息传感器数据的传输、预处理和存储、预测模型的训练以及预测本身都提出了特殊挑战。 尽管在收集相关数据或关于构建操作识别任务预测模型的方法方面已经进行了大量研究，但实际需求是需要Python包和函数，这些包和函数可以快速将数据转换为所需的结构，并构建和评估此类模型。 因此开发了maneuverRecognition包，以提供预处理、建模和评估所需的函数，还包含一个可修改的基于LSTM的网络结构。 该包的实现使用通过智能手机传感器记录的三个人的真实驾驶数据进行演示。 显示英文摘要

摘要： In the domain of vehicle telematics the automated recognition of driving maneuvers is used to classify and evaluate driving behaviour. This not only serves as a component to enhance the personalization of insurance policies, but also to increase road safety, reduce accidents and the associated costs as well as to reduce fuel consumption and support environmentally friendly driving. In this context maneuver recognition technically requires a continuous application of time series classification which poses special challenges to the transfer, preprocessing and storage of telematic sensor data, the training of predictive models, and the prediction itself. Although much research has been done in the field of gathering relevant data or regarding the methods to build predictive models for the task of maneuver recognition, there is a practical need for python packages and functions that allow to quickly transform data into the required structure as well as to build and evaluate such models. The maneuverRecognition package was therefore developed to provide the necessary functions for preprocessing, modelling and evaluation and also includes a ready to use LSTM based network structure that can be modified. The implementation of the package is demonstrated using real driving data of three different persons recorded via smartphone sensors.