In at the moment’s period of AI, accelerated innovation is an ever-present problem for companies. They’re concurrently going through funds and time-to-market constraints whereas buyer expectations proceed to mature and evolve, significantly in automotive, mobility, and transportation. The fitting expertise to face these challenges should drive transformation and ship extra worth in much less time.
Microsoft automotive, mobility, and transportation reference architectures
To assist companies in assembly these objectives, Microsoft introduced three mobility reference architectures at CES 2023. In 2024, we’re including 4 new reference architectures to our {industry} portfolio. These new assets assist our accomplice ecosystem assist their prospects in driving the way forward for mobility throughout analysis and improvement, manufacturing, provide chain and logistics, advertising and marketing, gross sales, and aftersales.
Microsoft mobility documentation
Profit from mobility with docs and supported reference architectures
Microsoft reference architectures for mobility
- Software program outlined car toolchain new
- Mobility Copilot new
- Azure Innovation Accelerator new
- Autonomous car operations
- Related fleets
- Unified view of the client new
- Digital promoting
Microsoft reference architectures are designed to supply construction and steering for challenge managers, enterprise architects, and IT managers to ship agile enterprise outcomes. Options using the mobility {industry} reference architectures can be found via certified companions and Microsoft {industry} options.
The software program outlined car (SDV) toolchain reference structure allows a contemporary cloud-native software program improvement toolchain leveraging robust developer tooling providers with further performance particular to automotive. The reference structure covers steady improvement, testing, and supply of high-quality software program.
This plug-and-play strategy supplies the next advantages:
- Reduces the time to onboard new builders and will increase code high quality utilizing generative AI.
- Accelerates improvement, testing, and validation of automotive software program by shifting left—testing earlier and extra usually within the improvement course of to enhance software program high quality and improvement pace.
- Reduces reliance on in-vehicle silicon with extremely configurable and versatile digital digital management unit (ECU) and virtualized {hardware} within the loop (HiL) environments on Microsoft Azure.
- Helps compatibility with edge and in-vehicle silicon by providing equal compute on Azure.
- Helps the validation course of by having a standard infrastructure for deploying software program artifacts from verification via software-in-the-loop (SiL) to check fleets and accumulating “attention-grabbing information” to drive modifications to the software program.
The SDV reference structure contains the next parts:
- Improvement tooling utilizing confirmed Microsoft instruments to extend developer productiveness and collaboration resembling GitHub, GitHub Copilot, Microsoft Dev Field, and Visible Studio Code. These instruments are extensible with automotive-specific performance from companions.
- The SDV improvement, validation, and integration reference structure supplies orchestration providers that assist you to handle deployment environments and goal configurations to speed up your testing and verification on digital ECU and digital HiL options within the cloud.
- Azure providers present foundational capabilities, resembling deployment environments, compute virtualization, and information storage. Microsoft Cloth supplies information and analytics providers.
- Azure networking supplies connectivity to on-premises, HiL validation environments.
- The Azure and GitHub marketplaces simplify integration of accomplice choices for tooling and digital photos.
Mobility Copilot reference structure
The Mobility Copilot reference structure allows numerous use instances throughout the worth chain to extend productiveness, unleash creativity, and drive innovation with AI. The structure helps eventualities in analysis and improvement, manufacturing, advertising and marketing and gross sales, in-vehicle providers, and after-sales or restore with innovative structure constructed on the most recent AI fashions with various UX paradigms for optimum buyer and worker journeys.
- Copilots for engineering necessities: AI-based assistant to empower engineers to optimize car efficiency and design from aerodynamics and gas effectivity to security and aesthetics.
- Copilots for manufacturing high quality and effectivity: AI-based assistant that will increase manufacturing effectivity by analyzing high quality information from improvement, buyer expertise, manufacturing, and different sources to detect high quality points.
- Digital buyer assistant: Conversational engagement for advertising and marketing, gross sales, and customer support channels to reply product-or-concern-related questions, qualify leads, drive gross sales, and enhance effectivity via automation.
- Onboard AI assistant: In-vehicle voice assistants to enhance pure language understanding and increase the vary of driver and operation interplay.
- Copilots for car diagnostics and restore: AI assistant for automotive restore workshops to enhance car diagnostics, streamline upkeep duties, and supply real-time insights to mechanics.
Mobility Copilots allow the next distinctive worth propositions:
- Greatest-in-class AI fashions: Multi-modal enterprise-grade fashions inside Azure based mostly on Azure OpenAI Service, for instance GPT 3.5 Turbo, GPT-4, and DALL-E expertise.
- One-base structure: The versatile base structure addresses a wide-array of use-cases for optimum effectivity and flexibility.
- Simple to plug-in: The structure will be seamlessly built-in into present architectures, offering swift deployments.
- Unified information sourcing: Seamlessly combine a number of information sources together with structured and unstructured information.
- Accountable and safe: Azure OpenAI Service situations are remoted from each different buyer—the info isn’t used to coach the AI mannequin and is protected by complete enterprise compliance and safety controls.
Corporations like Mercedes-Benz have already began to implement Microsoft AI-based options throughout the worth chain resembling manufacturing, gross sales and advertising and marketing, and in-vehicle providers. Different {industry} examples embrace GM, Amadeus, CarMax, and Air India.
Azure innovation accelerator reference structure
The Azure innovation accelerator (Azure IX) is an end-to-end cloud resolution offering fashionable, versatile, and safe Azure-based IT infrastructure that’s constructed and maintained by a extremely expert DevSecOps workforce. The answer will be applied inside six to eight weeks, facilitating quick innovation. This strategy optimizes time to worth and supplies a safe atmosphere for single or a number of corporations to collaborate with minimal information administration burden. This structure can be utilized for numerous eventualities that require agile collaboration together with accomplice ecosystem enablement, excessive safety environments, short-term high quality investments, quick-deployments, or restricted IT functionality.
This fast deployment resolution provides the next advantages:
- Turnkey resolution: Prepared-made state-of-the-art infrastructure based mostly on a Microsoft blueprint and atmosphere allows quick roll-out.
- Absolutely managed resolution: To make sure infrastructure is on the market and updated with out taxing buyer assets.
- Safety as a service: Microsoft supplies numerous managed safety modules out there via the platform to service the best safety calls for.
- Outlined governance mannequin: Outlined governance fashions permit trustful collaboration and joint management of the platform to guard belongings and IP.
- Versatile accomplice collaboration: Enabling onboarding of builders from completely different corporations with completely different insurance policies to turn into productive on one joint platform.
- Change built-In: The platform and the managed elements present a versatile change strategy based mostly on calls for.
- Deal with producing worth: Burden of upkeep, change, and administration is low by design via an agile collaboration mannequin.
- Cut up billing: The associated fee incurred will be distributed among the many companions via outlined break up.
The Azure innovation accelerator structure consists of the next parts:
- Azure IX is clustered in platform infrastructure and the corresponding DevSecOps builds and runs a dynamic Azure-based cloud atmosphere.
- The platform infrastructure consists of the obligatory Azure IX Resolution Core with embedded processes based mostly on agile ideas for safety and comfort.
- Azure IX supplies base packages for operations tailor-made to the client use case.
- Moreover the complete Azure and Microsoft product stack, the DevSecOps workforce can run and preserve third social gathering instruments and even buyer instruments by association. The workforce supplies rapid assist and determination for improvement and operations inside one workforce, a key differentiator amongst the competitors.
- The primary constructing blocks of Azure IX are proven within the diagram under.
Throughout the automotive {industry}, Azure IX can be utilized as a base platform to shortly advance all mobility providers together with SDV, Mobility Copilots, and autonomous car improvement. Considered one of Azure IX’s first profitable use instances has been in advancing autonomous driving improvement. Superior driving help methods (ADAS) expertise wants robust collaboration between numerous stakeholders within the improvement of recent capabilities that require a extremely versatile, safe atmosphere constructed for change.
Unified view of the client reference structure
The Microsoft unified view of the client reference structure brings all sources of buyer information collectively, enabling deep insights and hyper-personalized mobility use instances for automotive and industrial unique gear producers (OEMs) and sellers in journey, aviation, and in mobility-as-a-service. The shopper 360 blueprint units the inspiration for a unified buyer information platform (resembling information integration, pipelines, and consent handing) to mix related information sources into one single view of the client.
A few of the advantages of this structure embrace:
- Improved buyer expertise: The structure supplies a 360-degree view of the client, enabling companies to ship customized experiences throughout channels and touchpoints.
- Elevated effectivity: By consolidating buyer information sources, companies can cut back the effort and time required to entry and analyze buyer information with “zero extract, remodel, and cargo (ETL)” integration and “zero-data transfer”.
- Higher choice making: The structure allows companies to realize deep perception into buyer habits and preferences, which can be utilized to make data-driven selections.
- Elevated income: By delivering customized experiences and bettering buyer satisfaction, companies can enhance buyer loyalty and income.
- Safe perception: A personalised ID guards buyer insights making certain consent is dealt with clearly and seamlessly and insights are on the contact of the client’s digital fingertip.
Unified view of the client is predicated on a harmonized information mannequin utilizing Microsoft Cloth to supply buyer core information together with unified buyer id. The structure illustrates how stay buyer interactions are processed via occasion stream and real-time analytics, then built-in with advertising and marketing content material to create a complete buyer 360-degree profile via Microsoft Cloth. This profile is enriched with information from third-party advertising and marketing clouds and central advertising and marketing channels, which is then analyzed for insights and proposals utilizing Microsoft Dynamics 365 Buyer Insights.
Clients like Amadeus are leveraging buyer centric options to create extra selection for his or her prospects, higher high quality service, and smoother journey experiences.
Related fleets reference structure
The Related fleets reference structure allows sooner, decrease value, greater worth fleet administration options—resembling asset administration and area service—by simplifying worth extraction from linked car information, streamlining integration with enterprise methods, and facilitating specialised analytics.
The Related fleets integration framework allows companions to construct value-added options with distinctive capabilities, whereas additionally enabling the client to make the most of key options resembling Microsoft Cloth, Microsoft Dynamics 365, and Azure OpenAI Service. This eliminates the necessity for fractured and costly options constructed from a number of sources, permits sooner improvement and time to worth, and makes use of the Microsoft Cloud to scale back prices. With versatile, standards-based information ingestion, the structure helps present linked car options, OEM feeds, or information exchanges as acceptable. This flexibility helps to extend scope and cut back value of auto information acquisition.
The Related fleets reference structure is enabled by the next companions: Accenture, Cognizant, Related Automobiles, DSA, Hitachi Zero Carbon, HCL Applied sciences, Luxoft, Mojio, Netstar, STZRE, and TomTom. A stay instantiation of the ability of composing a number of accomplice options collectively is now out there and underpins a brand new demo proven first at CES 2024, as illustrated within the following graphic:
Autonomous car operations reference structure
The autonomous car operations (AVOps) reference structure supplies an built-in, end-to-end workflow for growing, verifying, and bettering ADAS and autonomous autos (AVs).
Business-leading Microsoft Azure cloud providers are offered to combine together with your device chain, whereas our companions full a powerful, industry-vetted world, safe, and hyper-scale cloud-based improvement platform that spans improvement, validation, runtime deployments, and suggestions loops.
No matter degree of autonomy, growing a self-driving software program stack and bringing a brand new automotive to market presents a collection of workflow challenges:
- Environment friendly ingestion of terabytes and even petabytes of knowledge each day generated by fleets of check autos.
- Software program and AI replace requires validation via simulation with large quantities of computing energy.
- Petabytes of knowledge and peta-scale computing is dear—delivering on time and inside funds is difficult.
- How do you precisely validate and check efficiency?
AVOps supplies a templatized digital testbed that may be spun up on demand, shared throughout all the worth chain, and used to shorten time-to-market. This strategy helps our automotive prospects remodel themselves into software program and AI corporations.
The structure additionally helps optimize operational ADAS and AV workflow coordination and have improvement, verification, and validation via:
- DataOps: From edge to cloud, the orchestration of petabytes of knowledge to assist parallel workstreams.
- DevOps: Scaling the continual integration (CI) and steady supply (CD) pipeline.
- MLOps: Scaling machine studying pipelines and integrating with CI and CD pipelines.
- ValidationOps: The power to precisely simulate software program and AI updates throughout all edge instances.
How generative AI may help
The white paper “Enhancing effectivity in AVOps with Generative AI” explores how generative AI may help cut back the complexity, value, and time of growing and testing autonomous driving methods throughout simulation, validation, optimization, and personalization. Use instances embrace producing artificial eventualities, validating sensor information, optimizing driving habits, and personalizing person expertise.
The AVOps reference structure supplies a collaborative, open framework constructed on frequent processes that helps automate, handle, and carefully monitor the validation processes for software program and {hardware} deployment. The next Microsoft companions present further value-added capabilities to this framework: Ansys, Akridata, Utilized Instinct, Cognata, dSPACE, and Linker Networks.
Digital promoting reference structure
The digital promoting reference structure allows reinvention of the standard buyer journey by reworking real-life areas into an immersive and interactive digital atmosphere that reaches a wider vary of consumers.
The reference structure allows distinctive, environment friendly, and immersive methods to have interaction with customers by unlocking the ability of the metaverse to enhance communications, gross sales, and operations. Enhanced by accomplice capabilities, focus areas embrace digital advertising and marketing, e-commerce, high-fidelity 3D rendering, and industry-specific methods. By integrating disparate information from the client, area service, and seller administration methods, improved visibility, collaboration, and information insights can drive extra customized, multi-channel buyer experiences. Via the digital promoting reference structure, cloud and accomplice capabilities, OEMs, sellers, and mobility, service suppliers now have the pliability to boost in-person interactions together with enabling new digital buyer engagement fashions.
The next announcement by Fiat highlights the transformative impression of digital promoting.
A gaggle of curated knowledgeable companions will drive innovation whereas offering excellence in omnichannel success, immersive and digital person experiences, and end-to-end course of protection. These companions embrace: Sitecore, Adobe, Touchcast, Annata, and Stella Automotive AI.
Reinventing the client journey
Microsoft in mobility and manufacturing industries
John Reed serves as Options Chief for the Manufacturing and Mobilities industries. He’s accountable for Microsoft’s world portfolio of cloud, edge & AI pushed options and partnerships in: Automotive; Discrete, Course of & Excessive-Tech Manufacturing; Aerospace; and Transportation & Logistics. With greater than 20 years of expertise within the Automotive {industry}, he is been concerned within the evolution of mobility applied sciences and options throughout producers, distributers, retailers, and mobility providers suppliers.
