maneuverRecognition -- A Python package for Timeseries Classification in the domain of Vehicle Telematics
This addresses a practical need for software tools to automate driving maneuver recognition, which can enhance insurance personalization and road safety, but it is incremental as it packages existing methods.
The authors tackled the lack of Python tools for time series classification in vehicle telematics by developing the maneuverRecognition package, which includes functions for preprocessing, modeling, and evaluation, and demonstrated it on real driving data from three individuals.
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.