ITSC 2024 Paper Abstract

Paper WeAT14.6

Kirchner, Sven (TU München), Purschke, Nils (Technical University of Munich), Wu, Chengdong (Technical University of Munich), Khan, Muhammad Aqib (Technical University of Munich), Dixit, Divye (TUM,IIT Mandi), Knoll, Alois (Technische Universität München)

AUTOFRAME: Software-Driven Integration Framework for Automotive Systems

Scheduled for presentation during the Poster Session "Validation, simulation, and virtual testing I" (WeAT14), Wednesday, September 25, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on November 4, 2024

Keywords Other Theories, Applications, and Technologies, ITS Field Tests and Implementation, Driver Assistance Systems

Abstract

The evolution of automotive technologies toward more integrated and sophisticated systems requires a shift from traditional distributed architectures to centralized vehicle architectures. This paper presents a novel framework that addresses the increasing complexity of Software Defined Vehicles (SDV) through a centralized approach that optimizes software and hardware integration. Our approach introduces a scalable, modular, and secure automotive deployment framework that leverages a hardware abstraction layer and dynamic software deployment capabilities to meet the growing demands of the industry. The framework supports centralized computing of vehicle functions, making software development more dynamic and easier to update and upgrade. We demonstrate the capabilities of our framework by implementing it in a simulated environment where it effectively handles several automotive operations such as lane detection, motion planning, and vehicle control. Our results highlight the framework's potential to facilitate the development and maintenance of future vehicles, emphasizing its adaptability to different hardware configurations and its readiness for real-world applications. This work lays the foundation for further exploration of robust, scalable, and secure SDV systems, setting a new standard for future automotive architectures.