Preserving Automotive Heritage: A Blockchain-Based Solution for Secure
Documentation of Classic Cars Restoration
- URL: http://arxiv.org/abs/2403.08093v1
- Date: Tue, 12 Mar 2024 22:06:57 GMT
- Title: Preserving Automotive Heritage: A Blockchain-Based Solution for Secure
Documentation of Classic Cars Restoration
- Authors: Jos\'e Murta and Vasco Amaral and Fernando Brito e Abreu
- Abstract summary: To be considered masterpieces, classic cars must be maintained in pristine condition or restored according to strict guidelines applied by expert services.
Using a design science research approach, we have developed a blockchain-based solution using Hyperledger Fabric.
This solution was validated and received positive feedback from various entities in the classic car sector.
- Score: 49.90846338134154
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Classic automobiles are an important part of the automotive industry and
represent the historical and technological achievements of certain eras.
However, to be considered masterpieces, they must be maintained in pristine
condition or restored according to strict guidelines applied by expert
services. Therefore, all data about restoration processes and other relevant
information about these vehicles must be rigorously documented to ensure their
verifiability and immutability. Here, we report on our ongoing research to
adequately provide such capabilities to the classic car ecosystem.
Using a design science research approach, we have developed a
blockchain-based solution using Hyperledger Fabric that facilitates the proper
recording of classic car information, restoration procedures applied, and all
related documentation by ensuring that this data is immutable and trustworthy
while promoting collaboration between interested parties. This solution was
validated and received positive feedback from various entities in the classic
car sector. The enhanced and secured documentation is expected to contribute to
the digital transformation of the classic car sector, promote authenticity and
trustworthiness, and ultimately increase the market value of classic cars.
Related papers
- AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios.
We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
arXiv Detail & Related papers (2024-03-26T04:27:56Z) - A Review on Blockchain Technologies for an Advanced and Cyber-Resilient Automotive Industry [0.0]
This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features.
The broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications.
Some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.
arXiv Detail & Related papers (2024-02-01T19:23:19Z) - Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients.
Existing works require central servers to retain the historical model parameters from distributed clients.
This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
arXiv Detail & Related papers (2024-01-29T07:04:48Z) - Driving Towards Inclusion: Revisiting In-Vehicle Interaction in
Autonomous Vehicles [5.0674776499043865]
The study's aim is to examine the user-centered design principles for inclusive HCI in self-driving vehicles.
Emerging technologies that have the potential to enhance the passenger experience are identified.
The paper proposes an end-to-end design framework for the development of an inclusive in-vehicle experience.
arXiv Detail & Related papers (2024-01-26T00:06:08Z) - The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry [72.10607978091492]
Automated/Autonomous Machine Learning (AutoML/AutonoML) is a relatively young field.
This review makes two primary contributions to knowledge around this topic.
It provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial.
arXiv Detail & Related papers (2022-11-08T10:42:08Z) - Connected Vehicle Platforms for Dynamic Insurance [0.0]
Many car manufacturers are adding additional services on top, so that more and more cars become connected vehicles and act like IoT sensors.
In this study, we analyse the maturity level of this new technology to build insurance products that would take vehicle usage into account.
Our results highlight that, while this technological innovation appears very promising in the future, the pricing, the lack of uniformity of data collected and the enrollment process are currently three pain points that should be addressed to offer large-scale opportunities.
arXiv Detail & Related papers (2022-08-01T14:30:18Z) - Trends in Vehicle Re-identification Past, Present, and Future: A
Comprehensive Review [2.9093633827040724]
Vehicle re-id matches targeted vehicle over-overlapping views in multiple camera network views.
This paper gives a comprehensive description of the various vehicle re-id technologies, methods, datasets, and a comparison of different methodologies.
arXiv Detail & Related papers (2021-02-19T05:02:24Z) - Vehicular Cooperative Perception Through Action Branching and Federated
Reinforcement Learning [101.64598586454571]
A novel framework is proposed to allow reinforcement learning-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs)
A federated RL approach is introduced in order to speed up the training process across vehicles.
Results show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.
arXiv Detail & Related papers (2020-12-07T02:09:15Z) - The Devil is in the Details: Self-Supervised Attention for Vehicle
Re-Identification [75.3310894042132]
Self-supervised Attention for Vehicle Re-identification (SAVER) is a novel approach to effectively learn vehicle-specific discriminative features.
We show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
arXiv Detail & Related papers (2020-04-14T02:24:47Z)
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.