Automatic Platform Configuration and Software Integration for Software-Defined Vehicles
- URL: http://arxiv.org/abs/2408.02127v1
- Date: Sun, 4 Aug 2024 19:54:03 GMT
- Title: Automatic Platform Configuration and Software Integration for Software-Defined Vehicles
- Authors: Fengjunjie Pan, Jianjie Lin, Markus Rickert,
- Abstract summary: This paper introduces a novel approach to automate platform configuration and software integration for software-defined vehicles (SDVs)
By leveraging model-based systems engineering (MBSE), our method automatically generates platform configuration and software integration plans.
The proposed system enables dynamic and flexible resource allocation while ensuring compliance with safety requirements.
- Score: 4.522485108591059
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the automotive industry, platform configuration and software integration are mostly manual tasks performed during the development phase, requiring consideration of various safety and non-safety requirements. This manual process often leads to prolonged development cycles and provides limited flexibility. This paper introduces a novel approach to automate platform configuration and software integration for software-defined vehicles (SDVs), shifting these activities from the development phase to runtime. Our approach features an integration manager that combines model-based methods and virtualization technologies to generate and execute deployment plans. By leveraging model-based systems engineering (MBSE), our method automatically generates platform configuration and software integration plans, which are then converted into deployment-ready formats using code generation techniques. Utilizing virtualization and container orchestration technologies, the proposed system enables dynamic and flexible resource allocation while ensuring compliance with safety requirements. Communication between the development and runtime platforms is facilitated via a REST API. A proof of concept was implemented on a simulated SDV platform with the Intel Whiskey Lake Board. This demonstration showcases the integration manager on an SDV with a central computer, highlighting the potential to shorten development cycles and adapt to diverse vehicle configurations.
Related papers
- CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS [0.0]
We propose CARLOS - an open, modular, and scalable simulation framework for the development and testing of software in C-ITS.
We provide core building blocks for this framework and explain how it can be used and extended by the community.
In our paper, we motivate the architecture by describing important design principles and showcasing three major use cases.
arXiv Detail & Related papers (2024-04-02T10:48:36Z) - Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow [3.2821049498759094]
We propose a novel model- and feature-based approach to development of vehicle software systems.
One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs)
The resulting pipeline is automated to a large extent, with feedback being generated at each step.
arXiv Detail & Related papers (2024-03-21T15:07:57Z) - Control and Automation for Industrial Production Storage Zone: Generation of Optimal Route Using Image Processing [49.1574468325115]
This article focuses on developing an industrial automation method for a zone of a production line model using the DIP.
The neo-cascade methodology employed allowed for defining each of the stages in an adequate way, ensuring the inclusion of the relevant methods for its development.
The system was based on the OpenCV library; tool focused on artificial vision, which was implemented on an object-oriented programming (OOP) platform based on Java language.
arXiv Detail & Related papers (2024-03-15T06:50:19Z) - AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism.
The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
arXiv Detail & Related papers (2024-02-21T04:11:28Z) - Emergent Software Service Platform and its Application in a Smart
Mobility Setting [2.2969236985898744]
Systems are evolving in DevOps processes in which heterogeneous actors act together on an open platform.
In this paper, we propose an architecture for such an emergent software service platform.
A software platform that implements this architecture with the underlying engineering methodology is demonstrated by a smart parking lot scenario.
arXiv Detail & Related papers (2023-08-16T06:51:23Z) - Developing an AI-enabled IIoT platform -- Lessons learned from early use
case validation [47.37985501848305]
We introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection.
This is complemented by insights and lessons learned during this early evaluation activity.
arXiv Detail & Related papers (2022-07-10T18:51:12Z) - Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
We instrument each machine by means of an external agent to generate user activity.
This agent combines both deterministic and deep learning based methods to adapt to different environment.
We also propose conditional text generation models to facilitate the creation of conversations and documents.
arXiv Detail & Related papers (2021-11-23T10:53:29Z) - YMIR: A Rapid Data-centric Development Platform for Vision Applications [82.67319997259622]
This paper introduces an open source platform for rapid development of computer vision applications.
The platform puts the efficient data development at the center of the machine learning development process.
arXiv Detail & Related papers (2021-11-19T05:02:55Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - iPaaS in Agriculture 4.0: An Industrial Case [0.0]
We propose a generic i architecture based on several open source solutions boasting integration, interoperability, and automated decision-making capabilities.
A proof-of-concept based on these solutions is presented, as well as a case study on MA"ISADOUR's grain storage process with a comparison with the currently human-operated tasks.
arXiv Detail & Related papers (2020-10-08T07:52:37Z)
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.