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In the mobility industry, product and service design and development represent the most substantial allocation within the IT budgets of many original equipment manufacturers (OEMs) and remain significant for all industry players. Recognizing the potential efficiencies and scalability that cloud services can bring, there's an increasing interest among these players to explore cloud-based solutions. While the transition to the cloud is underway, concerns related to data sensitivity and its overall business advantages have tempered the transition. Nevertheless, burst workloads and high-compute, large-scale workloads, such as high-performance computing (HPC), are spearheading the transition.

The following scenarios offer a set of solutions that help mobility customers harness the power of digital engineering to streamline product design and manufacturing, and use the software-defined vehicle (SDV) paradigm for continuous deployment and revenue streams. Further, we explore how connected fleets can transform data into valuable services and the role of cloud platforms in expediting autonomous (ADAS/AV) system development. These capabilities add up to an end-to-end data and software development platform for a wide range of next-generation mobility enterprises.

Digital engineering

Within manufacturing and mobility, digital engineering is a pivotal component in the evolving landscape of digital transformation. With a visible trend towards migrating and consolidating project lifecycle management (PLM) and computer-aided design (CAD) solutions to the cloud, manufacturers in the sector emphasize on PLM as an important tool to achieve product sustainability goals. The growing integration of internet of things (IoT) into product engineering and design processes complements the evolution of digital transformation. Central to these advancements is high-performance computing (HPC), which is profoundly influencing product and process innovation within the mobility framework.

The Microsoft vision for digital engineering focuses on empowering our customers. By championing a model-based enterprise, we emphasize informed decision-making using 3D models. We advocate for a single, authoritative product data source to infuse intelligence into engineering with innovative technology and promote barrier-free collaboration. Additionally, we see the transformation of workforce and culture as crucial to embracing digital engineering throughout the product and service lifecycle.

Microsoft supports top digital engineering use cases, such as the following:

  • Product lifecycle and design: Run PLM applications and CAD workloads in the cloud to gain agility, efficiency, and scalability. Use AI for the next frontier of product design and collaborate more broadly, effectively, and efficiently.
  • Digital twins and simulations: Create data representations of your physical products, assets, and factories. Simulate different scenarios for product design optimization, process improvement or factory setup decisions, with greater speed and with reduced data sampling.
  • Connected products: Embed connectivity and intelligence into your products based on a modern edge-to-cloud architecture. This use case allows new business models like product-as-a-service, a digital services ecosystem, plus product & service continuous optimization and targeting through usage and performance insights.  

The key Microsoft technologies that support the digital engineering use cases include:

Software-defined vehicle (SDV)

The automotive industry is undergoing a revolutionary transformation with the advent of new technologies that include connectivity, automation, and electrification. In this era of change, collaboration between software companies and automotive stakeholders is crucial to drive innovation and shape the future of mobility.

A software-defined vehicle (SDV) refers to a vehicle architecture that uses software-based solutions to control and manage the various aspects of the vehicle's functionality. In an SDV, critical functions that traditionally relied on tightly coupled hardware and software components are decoupled, virtualized, and implemented through software. The software implementation of the vehicle's critical functions allows greater flexibility, scalability, and customization throughout the vehicle's lifecycle.

The Microsoft strategy for software-defined vehicles centers on the following key areas:

  • Eclipse Partnership: We establish, contribute to, and promote an industry-wide open-source software (OSS) ecosystem in the SDV working group within the Eclipse foundation. We collaborate with our partners to help them commercialize distributions based on Eclipse software-defined vehicle (ESDV).
  • OEM Azure Integrations: We allow original equipment manufacturers (OEMs) and top-tier partners to develop custom SDV implementations by using a wide range of Azure IaaS and PaaS services, such as Messaging and Eventing, Security, DevOps, AI, and compute.
  • Supportability: We partner with Silicon, OS vendors, and ISVs to provide out-of-the-box seamless support for Eclipse-based and custom SDV distributions, across on-board (in-vehicle), and off-board (cloud) domains.
  • SDV Toolchain Evolution: We're developing an open, comprehensive, modular, and flexible SDV toolchain framework, which establishes intuitive metadata-driven models to describe dev, build, and modeling toolchains for various workload types. This toolchain framework implicitly allows our partner ecosystem to seamlessly plug in, preserve, and monetize their IP.

The following SDV use cases supported by Microsoft are directly associated to the foundational strategies:

  • In-vehicle digital twin and vehicle-to-cloud-to-vehicle digital twin synchronization: Allow the creation and synchronization of digital replicas of physical vehicles, ensuring real-time communication between the vehicle and the cloud.
  • Democratization of in-vehicle application development: Facilitate an accessible and inclusive platform, allowing a broader range of developers to create applications specifically for in-vehicle systems.
  • Modern cloud-native software development toolchain: Deliver an advanced, cloud-based toolchain, streamlining the software development process and ensuring optimal performance for vehicle-related applications.

The following are the key Microsoft technologies that support the SDV use cases:

For an in-depth overview of the Microsoft approach, see the SDV reference architecture.

Connected fleets

A connected fleet refers to a network of vehicles or potentially other assets with similar characteristics, such as mobile industrial equipment or EV charging stations that are equipped with communication technologies. The technologies allow each asset in a fleet to send telemetry data to the cloud and receive remote commands, enabling interaction with one another and with external systems. This connectivity allows real-time monitoring, management, and optimization of the fleet's operations, leading to increased efficiency, improved safety, and enhanced decision-making capabilities. The connectivity also provides flexible integration with business processes, helping companies optimize their resources, reduce costs, improve customer satisfaction, and enhance their competitive advantage in the market.

Microsoft supports connected fleet management through Azure Messaging, which allows real-time data collection, storage, and analysis from connected vehicles. By integrating partner capabilities and first-party offerings like Microsoft Fabric, Dynamics 365, and Azure Maps, fleet managers can optimize routes, monitor vehicle performance, and predict and service maintenance needs. Microsoft security solutions and custom software development capabilities also ensure secure, tailored, and compliant connected fleet solutions for various industries.

Connected fleets architecture diagram

The top connected fleets use cases supported by Microsoft and our mobility partner ecosystem include:

  • Dispatch, route and load optimization: Optimize delivery routes to reduce fuel consumption, react to traffic changes, and optimize and loading of cargo.
  • Personnel and driver management, safety, and payroll: Keep track of driver hours, training, and certifications.
  • Performance-based billing: Use data to calculate key performance indicators (KPIs) and deliver value for money.
  • Maintenance and repair optimization: Use predictive and prescriptive maintenance to provide the optimum activity level that avoids costly disruptions in operations
  • Energy or fuel optimization and fleet electrification: Understand usage patterns of the fleet and deliver the roadmap towards electrification.

The key Microsoft technologies that support the connected fleets use cases include:

For an in-depth overview of the Microsoft approach, see the Connected fleets reference architecture.

Autonomous (ADAS/AV)

The world of autonomous vehicle development is quickly growing as technology continues to evolve. Autonomous and advanced driver assistance systems (ADAS) are an important technology in the mobility industry. Apart from reshaping how vehicles operate and enhancing safety and convenience for end users, the autonomous and advanced driver assistance systems also enhance financial prospects for transportation operators. The vision of fully autonomous vehicles is a technical reality. The initial deployment of L2/L3 autonomous vehicles, as defined by SAE International, has already hit the mass consumer market, while L4/L5 robotaxis, buses, and last-mile delivery systems are in development and starting to receive regulatory approval for extended testing.

Automotive engineers and IT leaders considering building an ADAS/AV solution need to consider critical business challenges. Top challenges include:

  • Data logistics: Developers need to extract and process petabytes of data from a global scale.
  • Data sharing: Leaders need to ensure effective collaboration and data sharing across multiple teams.
  • Scaling: Developers must scale autonomous vehicle development and validate test fleet operations.
  • Safety and validation: Accurate assessment of vehicle perception safety is crucial.
  • Budget: Minimizing costs for data hosting and sharing while delivering within budget is a concern.

The top autonomous use cases supported by Microsoft and our mobility partner ecosystem include:

  • Driver assistance: This use case deals with providing enhanced safety through ADAS features like lane keep assistance, automatic braking, and pedestrian detection. The driver assistance technology usually operates on highways, and primary and residential roadways for a safer and more convenient commuting experience.
  • Congestion management: This use case focuses on intra-city traffic optimization using fully autonomous driving without assisted drivers. Using autonomous vehicles for congestion management alleviates traffic bottlenecks. Further, it ensures smoother flow by targeting vehicular movement within designated city zones and inter-city routes, such as the city to the airport.
  • Mid-mile delivery: This use case facilitates the seamless transfer of goods between major distribution hubs and retail centers without the need for assisted drivers, with the help of vehicles predominantly operating within specific in-city delivery routes and designated geographic regions.
  • Trucking and Construction: This use case streamlines operations by autonomously transporting goods between ports of entry and distribution hubs, primarily on highways and within port and container areas. Therefore, using autonomous vehicles in trucking and construction reduces the need for safety drivers and bolsters safety and efficiency.

The key Microsoft technologies that support the autonomous and ADAS use cases include:

For an in-depth overview into the Microsoft approach, see the AVOps reference architecture.