In immediately’s period of AI, accelerated innovation is an ever-present problem for companies. They’re concurrently dealing with price range 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 help 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 sources assist our accomplice ecosystem help their clients 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 venture 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 permits a contemporary cloud-native software program improvement toolchain leveraging sturdy 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 method gives 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 typically 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 amassing “attention-grabbing knowledge” to drive adjustments 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 gives orchestration providers that will let you 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 knowledge storage. Microsoft Material gives knowledge and analytics providers.
- Azure networking gives connectivity to on-premises, HiL validation environments.
- The Azure and GitHub marketplaces simplify integration of accomplice choices for tooling and digital pictures.
Mobility Copilot reference structure
The Mobility Copilot reference structure permits numerous use instances throughout the worth chain to extend productiveness, unleash creativity, and drive innovation with AI. The structure helps situations 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 newest 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 gasoline effectivity to security and aesthetics.
- Copilots for manufacturing high quality and effectivity: AI-based assistant that will increase manufacturing effectivity by analyzing high quality knowledge 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 develop 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 primarily based 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 adaptableness.
- Simple to plug-in: The structure could be seamlessly built-in into current architectures, offering swift deployments.
- Unified knowledge sourcing: Seamlessly combine a number of knowledge sources together with structured and unstructured knowledge.
- Accountable and safe: Azure OpenAI Service situations are remoted from each different buyer—the information is just not used to coach the AI mannequin and is protected by complete enterprise compliance and safety controls.
Firms 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 answer offering trendy, versatile, and safe Azure-based IT infrastructure that’s constructed and maintained by a extremely expert DevSecOps staff. The answer could be applied inside six to eight weeks, facilitating quick innovation. This method optimizes time to worth and gives a safe setting for single or a number of corporations to collaborate with minimal knowledge administration burden. This structure can be utilized for numerous situations that require agile collaboration together with accomplice ecosystem enablement, excessive safety environments, short-term high quality investments, quick-deployments, or restricted IT functionality.
This speedy deployment answer provides the next advantages:
- Turnkey answer: Prepared-made state-of-the-art infrastructure primarily based on a Microsoft blueprint and setting permits quick roll-out.
- Totally managed answer: To make sure infrastructure is offered and updated with out taxing buyer sources.
- Safety as a service: Microsoft gives numerous managed safety modules accessible 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 totally different corporations with totally different insurance policies to turn out to be productive on one joint platform.
- Change built-In: The platform and the managed parts present a versatile change method primarily based on calls for.
- Give attention to producing worth: Burden of upkeep, change, and administration is low by design via an agile collaboration mannequin.
- Cut up billing: The price incurred could be distributed among the many companions via outlined cut 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 setting.
- The platform infrastructure consists of the necessary Azure IX Resolution Core with embedded processes primarily based on agile ideas for safety and comfort.
- Azure IX gives base packages for operations tailor-made to the client use case.
- In addition to the total Azure and Microsoft product stack, the DevSecOps staff can run and preserve third get together instruments and even buyer instruments by association. The staff gives fast help and determination for improvement and operations inside one staff, a key differentiator amongst the competitors.
- The primary constructing blocks of Azure IX are proven within the diagram under.
Inside the automotive {industry}, Azure IX can be utilized as a base platform to rapidly advance all mobility providers together with SDV, Mobility Copilots, and autonomous car improvement. One in all Azure IX’s first profitable use instances has been in advancing autonomous driving improvement. Superior driving help programs (ADAS) expertise wants sturdy collaboration between numerous stakeholders within the improvement of latest capabilities that require a extremely versatile, safe setting constructed for change.
Unified view of the client reference structure
The Microsoft unified view of the client reference structure brings all sources of buyer knowledge collectively, enabling deep insights and hyper-personalized mobility use instances for automotive and industrial authentic tools producers (OEMs) and sellers in journey, aviation, and in mobility-as-a-service. The client 360 blueprint units the inspiration for a unified buyer knowledge platform (resembling knowledge integration, pipelines, and consent handing) to mix related knowledge sources into one single view of the client.
Among the advantages of this structure embrace:
- Improved buyer expertise: The structure gives a 360-degree view of the client, enabling companies to ship personalised experiences throughout channels and touchpoints.
- Elevated effectivity: By consolidating buyer knowledge sources, companies can cut back the effort and time required to entry and analyze buyer knowledge with “zero extract, rework, and cargo (ETL)” integration and “zero-data transfer”.
- Higher choice making: The structure permits companies to realize deep perception into buyer conduct and preferences, which can be utilized to make data-driven choices.
- Elevated income: By delivering personalised experiences and enhancing 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 knowledge mannequin utilizing Microsoft Material to supply buyer core knowledge together with unified buyer id. The structure illustrates how reside 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 Material. This profile is enriched with knowledge from third-party advertising and marketing clouds and central advertising and marketing channels, which is then analyzed for insights and suggestions utilizing Microsoft Dynamics 365 Buyer Insights.
Prospects like Amadeus are leveraging buyer centric options to create extra selection for his or her clients, higher high quality service, and smoother journey experiences.
Related fleets reference structure
The Related fleets reference structure permits quicker, decrease value, increased worth fleet administration options—resembling asset administration and discipline service—by simplifying worth extraction from linked car knowledge, streamlining integration with enterprise programs, and facilitating specialised analytics.
The Related fleets integration framework permits companions to construct value-added options with distinctive capabilities, whereas additionally enabling the client to benefit from key options resembling Microsoft Material, Microsoft Dynamics 365, and Azure OpenAI Service. This eliminates the necessity for fractured and costly options constructed from a number of sources, permits quicker improvement and time to worth, and makes use of the Microsoft Cloud to scale back prices. With versatile, standards-based knowledge ingestion, the structure helps current linked car options, OEM feeds, or knowledge exchanges as applicable. This flexibility helps to extend scope and cut back value of car knowledge acquisition.
The Related fleets reference structure is enabled by the next companions: Accenture, Cognizant, Related Vehicles, DSA, Hitachi Zero Carbon, HCL Applied sciences, Luxoft, Mojio, Netstar, STZRE, and TomTom. A reside instantiation of the facility of composing a number of accomplice options collectively is now accessible 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 gives an built-in, end-to-end workflow for growing, verifying, and enhancing ADAS and autonomous automobiles (AVs).
Trade-leading Microsoft Azure cloud providers are supplied to combine together with your instrument chain, whereas our companions full a robust, 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 automobile to market presents a collection of workflow challenges:
- Environment friendly ingestion of terabytes and even petabytes of knowledge each day generated by fleets of take a look at automobiles.
- Software program and AI replace requires validation via simulation with huge quantities of computing energy.
- Petabytes of knowledge and peta-scale computing is dear—delivering on time and inside price range is difficult.
- How do you precisely validate and take a look at efficiency?
AVOps gives a templatized digital testbed that may be spun up on demand, shared throughout the complete worth chain, and used to shorten time-to-market. This method helps our automotive clients rework 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 help 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 might help
The white paper “Enhancing effectivity in AVOps with Generative AI” explores how generative AI might help cut back the complexity, value, and time of growing and testing autonomous driving programs throughout simulation, validation, optimization, and personalization. Use instances embrace producing artificial situations, validating sensor knowledge, optimizing driving conduct, and personalizing consumer expertise.
The AVOps reference structure gives a collaborative, open framework constructed on widespread processes that helps automate, handle, and intently 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 permits reinvention of the standard buyer journey by reworking real-life areas into an immersive and interactive digital setting that reaches a wider vary of shoppers.
The reference structure permits distinctive, environment friendly, and immersive methods to interact with customers by unlocking the facility 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 programs. By integrating disparate knowledge from the client, discipline service, and seller administration programs, improved visibility, collaboration, and knowledge insights can drive extra personalised, multi-channel buyer experiences. Via the digital promoting reference structure, cloud and accomplice capabilities, OEMs, sellers, and mobility, service suppliers now have the flexibleness to boost in-person interactions together with enabling new digital buyer engagement fashions.
The next announcement by Fiat highlights the transformative influence of digital promoting.
A bunch of curated knowledgeable companions will drive innovation whereas offering excellence in omnichannel success, immersive and digital consumer 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.