开发工具包，自动识别车辆驾驶动作，提升保险与安全。
maneuverRecognition -- A Python package for Timeseries Classification in the domain of Vehicle Telematics
- 提供数据预处理、建模与评估一体化的Python工具包
- 基于LSTM网络结构，支持实时驾驶行为分类
- 适用于智能交通与车险场景，可快速部署
在车联网领域，驾驶行为的自动化识别用于分类与评估驾驶习惯，不仅有助于个性化保险定价，还能提升道路安全、减少事故与燃油消耗，推动绿色驾驶。该任务需持续进行时间序列分类，对数据传输、预处理、存储、模型训练与预测带来特殊挑战。尽管已有大量研究聚焦于数据采集与预测模型构建，但缺乏实用的Python工具包来快速转换数据结构并建立评估模型。为此，本文开发了maneuverRecognition工具包，集成数据预处理、建模与评估功能，并内置可修改的LSTM神经网络结构。通过三名驾驶员使用智能手机传感器采集的真实驾驶数据进行了实证演示。
原文摘要 · Abstract (English)
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.
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