A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
- URL: http://arxiv.org/abs/2202.04411v1
- Date: Wed, 9 Feb 2022 11:45:38 GMT
- Title: A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
- Authors: Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran
Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches,
Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme
- Abstract summary: Volkswagen Financial Services (VWFS) aims to leverage existing proprietary data to enhance existing and derive new business processes.
We propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS.
- Score: 4.882606785609997
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Machine learning is being widely adapted in industrial applications owing to
the capabilities of commercially available hardware and rapidly advancing
research. Volkswagen Financial Services (VWFS), as a market leader in vehicle
leasing services, aims to leverage existing proprietary data and the latest
research to enhance existing and derive new business processes. The
collaboration between Information Systems and Machine Learning Lab (ISMLL) and
VWFS serves to realize this goal. In this paper, we propose methods in the
fields of recommender systems, object detection, and forecasting that enable
data-driven decisions for the vehicle life-cycle at VWFS.
Related papers
- MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse.
Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process.
Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
arXiv Detail & Related papers (2024-10-25T16:20:46Z) - QoS prediction in radio vehicular environments via prior user
information [54.853542701389074]
We evaluate ML tree-ensemble methods to predict in the range of minutes with data collected from a cellular test network.
Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles.
arXiv Detail & Related papers (2024-02-27T17:05:41Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z) - A Survey of Federated Learning for Connected and Automated Vehicles [2.348805691644086]
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain.
Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles.
arXiv Detail & Related papers (2023-03-19T14:44:37Z) - Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio
Access Technologies [56.77079930521082]
We have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies.
The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies.
We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage.
arXiv Detail & Related papers (2022-12-20T15:26:39Z) - 5G Features and Standards for Vehicle Data Exploitation [0.0]
5G can enable car-captured data to feed innovative applications and services deployed in the cloud.
This paper identifies and discusses the relevance of the main 5G features that can contribute to a scalable, flexible, reliable and secure data pipeline.
arXiv Detail & Related papers (2022-04-13T07:33:50Z) - From Distributed Machine Learning to Federated Learning: A Survey [49.7569746460225]
Federated learning emerges as an efficient approach to exploit distributed data and computing resources.
We propose a functional architecture of federated learning systems and a taxonomy of related techniques.
We present the distributed training, data communication, and security of FL systems.
arXiv Detail & Related papers (2021-04-29T14:15:11Z) - Data-Driven Aerospace Engineering: Reframing the Industry with Machine
Learning [49.367020832638794]
The aerospace industry is poised to capitalize on big data and machine learning.
Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
arXiv Detail & Related papers (2020-08-24T22:40:26Z) - Federated Learning in Vehicular Networks [41.89469856322786]
Federated learning (FL) framework has been introduced as an efficient tool with the goal of reducing transmission overhead.
In this paper, we investigate the usage of FL over centralized learning (CL) in vehicular network applications to develop intelligent transportation systems.
We identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management.
arXiv Detail & Related papers (2020-06-02T06:32:59Z) - Road Quality Analysis Based on Cognitive Internet of Vehicles (CIoV) [0.6345523830122167]
This research proposal aims to use cognitive methods to analyze the quality of roads based on the new proposed technology called Cognitive Internet of Vehicles (CIoV)
The proposed system can be used as an additional service of autonomous car companies or as a mobile application for ordinary usages.
arXiv Detail & Related papers (2020-04-16T09:59:45Z)
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.