Toward automated travel
Keeping the connected vehicle industry running with a holistic integrated health management system.
With the rise in electric vehicles (EVs) and the evolving connected car ecosystems, it’s time to rev up the engines on vehicle health monitoring. What we need today is a holistic integrated vehicle health management (IVM) system—powered by technologies such as IoT, AI-ML, and cloud—continually monitoring and integrating information from all the systems of a vehicle and from the expanding connected ecosystems. The goal is automated detection, diagnosis and prognosis of faults and maintenance requirements in a vehicle.
Just like regular servicing, such health checkups with an IVM platform throughout the vehicle life cycle is critical for today's vehicles fitted with complex networks of telematic control units, electronic control units, and control area network (CAN) bus architecture. The platform must provide both current diagnostics and future prognostics to optimize after-sales maintenance, warranty, and service support.
There’s no doubt that it’s a growing market—the IVM market is projected to touch USD 28 billion by 2027, accelerating at a compound annual growth rate of 12%. Apart from helping maintain the health of vehicles, another reason for their growing popularity is the potential for IVMs to drive automation in the industry.
When it to comes the level of automation in IVMs, on a scale of 0-5, the Society of Automotive Engineers (SAE) views most automotive manufacturers being in the 0-2 range (partial driving automation) based on their current state of diagnostics. A notable exception is Tesla, with their diagnostics rated 4-5 (high to full driving automation). With most vehicles becoming connected, there is an ever increasing need to support auto manufacturers to achieve 4-5 automation levels.
Data, handled with care
Driving accuracy and on-time performance of data
Another key challenge IVMs can help address is meeting the technological, privacy, and real-time data needs of the connected car market. Take, for instance, the millions of IoT devices in connected vehicles and the billions of messages streaming from these devices every day. IVMs can bring about a streamlined and well-managed approach to handling such massive volumes of data, while maintaining user privacy and minimizing bandwidth cost of data transmission.
Also, we know that to drive value from data, accuracy, speed, and accessibility are key. IVM platforms can help maximize value from data by optimizing end-to-end data processing time, ensuring reliability of information, and making sure that information is conveyed in a user-friendly fashion to aid decision making. So, how do we build a holistic IVM solution?
Driven by cloud and IoT
The continually evolving connected vehicle journeys mandate a cloud-based platform to manage the sprawling technology landscape.
As the enabler of various other new technologies, cloud, in combination with a service-oriented microservices architecture, can serve as the digital backbone of IVM systems. Add to it AI and ML, and you get edge and high-performance computing. The solution needs to leverage the latest advancements in cybersecurity to fight new threats and data vulnerability. Gigabit ethernet and other local storage (and compute) capabilities can also be leveraged.
To build a holistic IVM solution that will serve as the platform for connected, autonomous, shared, and electric vehicle ecosystem, here are a few key design considerations and features that all stakeholders in the ecosystem—OEMs, service teams, other external third-party suppliers—need to focus on.
Base serverless cloud PaaS platform supporting modular and scalable features such as microservices architecture, edge computing, a robust data lake framework, and is event-driven and fault-tolerant.
Automotive cyber security framework for securely onboarding and managing IoT devices at scale and providing multi-level protection from edge to cloud. The framework should cover identification of threats and protection against them; detecting anomalies; response, mitigation and recovery.
Software-defined vehicle architecture with functional features such as collection of real-time telemetry data, remote diagnostics with command and control, building digital twins for device health monitoring, fleet management, software and firmware updates over the air, anomaly detection, EV battery management and complete vehicle life cycle management.
Advanced services enablement with TinyML, AI at the edge, advanced machine learning models, and data analytics for predictive maintenance. Business reporting, dashboards, digital twin enablement—all with agile automated product delivery and deployment using DevOps and CICD—can be other focus areas.
In this context, cloud hyperscalers such as AWS, their related services, and partner ecosystems provide just the right components for a feature packed, powerful IVM platform. Whether it is enabling edge computing and data ingestion with AWS IoT Core, IoT rules, IoT device shadow, AWS IoT Device Management and AWS IoT Greengrass or data streaming with Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, building a microservice framework with AWS Lambda, AWS ECS, and AWS Fargate. Not to forget creating a data lake with services such as Amazon S3, Amazon Timestream, Amazon DynamoDB and creating Data Analytics framework with Amazon EMR, Amazon Athena.
The way forward
Cloud is lifting the world toward the future of connected mobility.
The advent of software defined vehicles, along with autonomous systems, is driving automotive manufacturing companies to leverage IoT ecosystems and next-generation technologies such as edge computing, cloud data lake, AI and ML. The way forward for OEM companies and suppliers is to design and develop a resilient, scalable, and secure IVM platform that provides a rich customer experience, ensures traveler’s safety, and opens up new revenue opportunities.
Learn more about how you can do that with TCS and AWS.