Software-Defined Vehicles Explained
A growing number of customers now anticipate receiving the same level of experience from their cars as they do from other smart gadgets. They want a car that can run its operations, add features, and enable new ones mostly or totally through software. They want complete integration into their digital life.
The demand for sophisticated features in cars is rising, which is being fueled by stricter auto safety laws, more money being spent on R&D, and improved connectivity and navigation. However, what really constitutes an Software-Defined Vehicles, and what is the architecture that underpins the vehicle to offer automation, personalization, and connectivity?
The SDV condensed
In an Software-Defined Vehicles, the car acts as the technological cornerstone for next advancements, a command center for gathering and arranging enormous amounts of data, utilizing AI to gain insights, and automating deliberate actions. By separating software from hardware, the Software-Defined Vehicles enables constant communication, automation or autonomy, and updates and upgrades. It engages with its surroundings, picks up knowledge, and backs service-oriented business models. Onboard electronics develop at the same time from standalone electronic control units to high-performance computers with improved integration and performance.
The Software-Defined Vehicles architecture up close
The layer of infrastructure
In addition to the vehicle, this layer also consists of numerous OEM backend systems, roadside devices, telco equipment, smart city systems, and other such components. All of these components are a part of a cycle wherein vehicle data is utilized for services, development, and operation. Over-the-air updates are used to install new software on automobiles based on insights gleaned from this data.
The platform layer of the hybrid cloud
According to the IBM strategy, a unified platform built on Linux and Kubernetes extends from the car to the edge of the backend system. Red Hat Enterprise Linux and Red Hat Openshift enable it, enabling flexible software distribution in the form of software containers in line with the “build once, deploy anywhere” philosophy. Before the software is simply installed into the infrastructure or vehicle, it might be designed and tested in the backend. This all adds up to an incredible level of versatility.
Better maintainability and portability of software are achieved through standardization through abstraction of application software in the form of containers, which boosts developer productivity. The IBM Embedded Automotive Platform, a Java runtime designed for in-vehicle use, and the IBM Edge Application Manager, which enables OEMs to scale and operate edge solutions autonomously, round out the hybrid cloud strategy.
The platform layer of the hybrid cloud
AI models have long been crucial to the operation of car systems like ADAS/AD. To produce safer and more customized cars, several OEMs, like Honda, use AI for knowledge management. AI is now being used in cybersecurity to examine incoming security events and incidents, as well as in telematics data analysis to obtain insights into driving experiences, with regard to vehicle operation.
These days, generative AI can produce software source code, architecture models, and test cases autonomously, which can significantly improve Software-Defined Vehicles development and operation. To handle different optimized foundation models for each use case, create custom-specific foundation models based on customer proprietary standards, and prevent engineering data from being included into publicly available open source foundation models that rivals might exploit, an AI and data platform like IBM Watsonx is needed. Furthermore, OEMs can maximize the deployment and utilization of AI models in edge devices, like cars, thanks to solutions like IBM Distributed AI API.
The layer of security
OEMs are progressively using a zero-trust cybersecurity framework to combat external and internal threats in corporate, in-vehicle, and development contexts. The Vehicle Security Operation Center, which uses IBM Security QRadar Suite for threat detection and security orchestration, automation, and response, is a key component of vehicle security.
OEMs must also encrypt all conversations that take place inside of cars as well as outside of them. The IBM Enterprise Key Management Foundation can help with this. Last but not least, IBM Security X-Force Red offers specialized automobile testing services.
The layer of AI products
Agile software development in a contemporary CI/CD environment is made possible for the automotive industry by a contemporary development platform like IBM Engineering Lifecycle Management. It offers model-based system engineering and testing, traceable requirements engineering, data-driven insights application, collaboration facilitation, complexity management for products, and compliance assurance. Moreover, Watsonx platforms facilitate AI engineering, which makes customized client experiences possible.
As seen in this Continental case study, engineering data management solutions assist clients in organizing the vast amounts of data required for the development of autonomous driving. Automation and orchestration of network operations are made possible by intelligent platforms such as IBM Cloud Pak for Network Automation. This is especially important for Telcos in the infrastructure. Manufacturers can develop their connected car use cases with the assistance of IBM Connected car Insight on the backend.
Additionally,Software-Defined Vehicles need a wide range of specialized technologies from several suppliers, which is why ecosystem cooperation is crucial to the SDV design.
In the end, each element of the architecture has a distinct function in guaranteeing the optimal experience for both drivers and passengers, thereby establishing the Software-Defined Vehicles as the next development in the automotive sector.
