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

• URL: http://arxiv.org/abs/2506.23147v1
• Date: Sun, 29 Jun 2025 08:56:19 GMT
• Title: maneuverRecognition -- A Python package for Timeseries Classification in the domain of Vehicle Telematics
• Authors: Jonathan Schuster, Fabian Transchel,
• Abstract summary: maneuverRecognition package provides functions for preprocessing, modelling and evaluation.<n>Implementation of the package is demonstrated using real driving data of three different persons recorded via smartphone sensors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions [32.37902846268263]
  In R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.
  arXiv  Detail & Related papers  (2024-05-02T23:24:27Z)
• BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving [24.123577277806135]
  We pioneer a novel behavior-aware trajectory prediction model (BAT) Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets.
  arXiv  Detail & Related papers  (2023-12-11T13:27:51Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data [40.43737902900321]
  We tackle the problem of workload estimation from driving performance data. Key environmental factors that induce a high mental workload are identified via video analysis. A supervised learning framework is introduced to profile drivers based on the average workload they experience. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload.
  arXiv  Detail & Related papers  (2023-03-26T13:15:44Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration [31.908276711898548]
  We present a vision-based framework for recognizing secondary driver behaviours based on visual transformers and an augmented feature distribution calibration module. Our framework consistently leads to better recognition rates, surpassing previous state-of-the-art results of the public Drive&Act benchmark on all levels.
  arXiv  Detail & Related papers  (2022-03-02T08:14:06Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation [11.007092387379076]
  We develop and train take-over time (TOT) models that operate on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views. We show that a TOT model supported by augmented data can be used to produce continuous estimates of take-over times without delay.
  arXiv  Detail & Related papers  (2021-07-27T16:39:50Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.