2025/06/29 by Jonathan Schuster, Schuster, Jonathan, F. W. G. Transchel +1
Engineering · Psychology · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sleep and Work-Related Fatigue #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2506.23147
openalex publication_date 2025/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.