IoT and AI in automotive - image of vehicles at night

Use Cases: Utilising IoT & AI in Automotive

Leveraging Big Data and AI approaches for process automation, predictive maintenance, and anomaly detection are at the forefront of R&D efforts in the automotive industry. Yet these are only part of the picture. Driving true optimization in the long term requires the combination of the Internet of Things (IoT), Big Data, and Artificial Intelligence strategies. Once these three components — IoT, Big Data, and AI — are sufficiently addressed across the value chain, automotive companies can make full use of AI and machine learning (ML) in a sustainable manner.

The adoption of AI and machine learning techniques combined with IoT technologies allows automotive companies to optimize their fleets, determine the conditions that impact performance, and improve passenger experience & safety, automating as much as possible along the way. 

Mobile IoT is on the rise, too. The more connected IoT devices that can track and analyze vehicle movement and the conditions under which anomalous behavior occurs, the better will automotive companies be able to understand how and where to optimize for maximum impact.

Why does this matter? Here are some key facts:

  • The average automotive manufacturer deals with some 800 hours of downtime annually, which means about $1 million in lost revenue.
  • Back in 2014, over 64 million vehicles in the USA were taken off the market due to defects. 
  • The automotive supply chain is decades old and highly fragmented. An immediate challenge is to renew, consolidate, and eliminate failures.

At the same time, demand for automotive products does not appear to be shrinking:

  • Connected car shipments are expected to rise to over 77 million by 2025.
  • Self-driving technology will be the biggest driver for AI adoption in transportation, presenting a $556 billion opportunity by 2026.
  • Over 14 million semi- or fully autonomous vehicles will be on the roads in the US by 2025.

What is the role of IoT & AI in all this? The key to surviving in this complex mesh is insight consolidation and high-quality data. 

Eventually, every automotive company will have to reinvent itself as a data-driven company.  And IoT & AI platforms are here to help. 

Combining IoT & AI on a single platform

So how does it work? To utilize the combined powers of IoT & AI (AIoT), you can connect your vehicles and related assets to a single platform. This is how you can begin to generate insights in real-time and near real-time. Once your vehicles are connected, you begin to extract and load data to the platform for further analysis. Right on the platform, you can combine data coming from multiple disparate data sources and consolidate it for insight generation. Here are the steps:

  1. Cleanse and transform the data coming from vehicles and machines in real-time or near real-time.
  2. Build machine learning algorithms. Once the data is transformed into high-quality data that can form long-term data histories, you can start building your machine learning algorithms using that data. 
  3. Get ready for insight generation. Or get started with data mining for greater insight and visualize the insights or ML model performance in customizable dashboards. This is how you get a view of the bigger picture within the processes at hand. 
  4. Deploy. Taking the insights generated on the basis of the data and the ML algorithms, you build IoT apps to deploy to production. From within the platform, ideally, these can be distributed across various car fleets across the globe at the click of a button. 
  5. Monitor & update. IoT & AI platforms (also known as AIoT platforms) allow you to establish iterative processes that involve the continuous monitoring of assets and performance parameters. This allows you to adjust to changing conditions and roll out app updates instantly. 
showing the stages of the full IoT & AI development process to be covered by just one platform
Figure 1. The end-to-end IoT & AI development process on the Record Evolution platform

To learn more about the potentials of industry-standard platforms that combine IoT and AI services, see Bringing AIoT to a Platform: Why Is This Important?

IoT & AI application areas in automotive

IoT data analytics in automotive keep organizations on the cutting edge by streamlining and improving both vertical and horizontal processes. This involves process automation and keeping up with advances in lean manufacturing, optimizing product design, addressing safety and performance concerns through anomaly detection, all the way to supply chain or sales quandaries, and, ultimately, innovative product development. 

IoT & AI is a new force in automotive that makes everything leaner, at every stage, and is ultimately geared towards a better consumer experience.

So what are the biggest impact areas for automotive manufacturing? Further, where do the combined forces of IoT & AI, also known as AIoT, make all the difference?

Automation in car manufacturing

Machine learning and AI play a key role in optimizing processes through automation. This involves operational streamlining, shortening production cycles, and removing procedural steps that previously involved the manual labor of human actors. Optimization on the operative level makes it possible for automotive companies to focus their creative energies on Research & Development, strategy, and system improvements.

Predictive car maintenance 

Custom machine learning is a typical application area to leverage the combined powers of IoT & AI. Here machine learning algorithms allow for the timely detection of wear and tear. Predictive maintenance is not just that, however. Thanks to data visualization dashboards that show the evolution of assets in real-time, robust AIoT platforms help you create the foundation for granular, highly customized alerting mechanisms, lifecycle monitoring, and the oversight of asset amortization. This is not simply about predicting vehicle failures ahead of time. It is about optimizing asset lifecycles and conducting maintenance in a sustainable, anticipatory manner. 

Anomaly detection

A special subcategory of predictive maintenance, anomaly detection helps you eliminate blind spots and investigate the underlying causes behind non-typical events. Combining IoT & AI helps you detect, prioritize, and combat threats to the smooth operation of vehicles.

Read more about our collaboration with Continental AG: Optimizing Brake Systems with the Record Evolution Platform. You will find out how we developed techniques for identifying and combating brake squeal noises using Big Data and AI approaches on IoT data collected from test vehicles.

User experience: connected cars

Connected car IoT offerings are on the rise. In-car marketplaces making it possible for consumers to supplement the driving experience with a wealth of services. These may range from personal retail therapy, wellness and well-being features offered straight from the convenience of a personal connected vehicle, multimedia offerings encompassing both entertainment and safety improvements, as well as a variety of comfort enhancement scenarios available from within a vehicle’s cockpit. 

The powers of IoT and AI hold the potential to “push the boundaries of personalization” and thus thoroughly transform the consumer experience through connectivity. Features-on-demand will have a positive impact on consumer convenience and will pave the way towards a full multimedia experience for both driver and passengers while working towards optimal vehicle performance and safety. At the same time, connected cars and the enhanced consumer experience will uncover new and unexpected business models for automotive companies.

Read more about our collaboration with Continental AG: Networked Mobility: New Approaches to Human-Machine Interaction in Industrial UI. You will learn how we worked on an integrated cockpit display for a new driving experience in future mobility scenarios. 

Optimizing the automotive supply chain

When IoT & AI are combined on a single platform, automotive companies can begin to leverage the full potential of their data efforts. And this includes supply chain optimization. IoT & AI platforms help enterprises simplify process flows, enhance collaboration to combat silos, and safeguard agility while making sure safety and accountability are still written large. 

IoT&AI-driven processes in supply chain operations are the result of concerted enterprise-wide efforts. So a single collaboration platform to unify insights, explore all relevant data, and monitor process-critical activities from a single venue is a necessary step towards process improvement. The IoT & AI platform enables real-time analytics and data visualization for faster reporting and decision-making. Machine learning algorithms help with operationalization issues, and security concerns are addressed all along the way.

Read more about the collaborative potentials unlocked with the Record Evolution platform here: “IoT Collaboration: The New Power in the Internet of Things”. 

The IoT & AI process from the edge to the cloud

So how about the complex confluence of IoT and AI capabilities? How do these work together and what processes do they account for? 

The role of IoT in collecting car data 

Again, the potentials harnessed by AI-powered systems are immense. To tap into these potentials, you need to first be able to read out data from your vehicles and machines. It is often the case that this data is siloed and hard to access. Yet collecting this data is the first crucial component in the process of building ML and AI solutions.

Using hardware-agnostic IoT platforms powered by container technology, today you can turn anything into an IoT edge device. This makes you fully equipped for operating in brownfield environments. Retrofitting has never been easier. It is possible to access any vehicle or machine to read out data from a variety of legacy devices.  

And the first step along the way is to make your vehicles IoT-ready:

diagram showing edge device deployments to any number of connected vehicles
Figure 2. Deploy IoT edge devices to any number of cars

The role of collected IoT data in analytics 

Once you have collected the needed data from your vehicles and machines, it has to be cleansed, modelled, and transformed. The data is turned into high-quality data that is readily available for long-term use. You can explore datasets coming from many different IoT data sources. On the basis of the insights gained out of these, build and deploy data solutions for further insights. In this way, an iterative cycle is formed. 

When you connect your assets with the IoT & AI platform (AIoT platform), you are continually receiving fresh IoT data from your fleets. You are using the data to improve on your data solutions. This is where you can get truly creative with IoT & AI, capturing more value from your existing IoT infrastructures. 

But to get there, you first need to collect IoT data from your fleets. Then you set up device management processes to safeguard the availability of that data. And you make sure that your fleets are connected to the IoT & AI platform whenever you need them to be:

diagram showing connecting cars to the IoT and AI platform
Figure 3. Connecting to the IoT and AI platform

Getting creative with IoT & AI

Once your fleets are connected to the platform, you can start using their data. You design your own AI solutions and deploy them to production for more insight generation in near real-time. You create and automate workflows to assist you in getting on top of your data, monitor your fleets to prevent unwanted behavior, update your ML models over the air, and collaborate with peers across the globe to enhance your existing solutions. 

Possible AI development scenarios may involve customized data collection apps (that is, machine learning models packaged as IoT apps), anomaly detection, maintenance notification, or event recognition applications that are rolled out directly to the IoT edge. And again, using the data from vehicles at the IoT edge, you continually improve on the apps and release updates in short iterative cycles.  

IoT and AI in automotive: diagram for developing AI in short cycles
Figure 4. Remote AI development in short iterative cycles

This is how you ultimately drive value. You cover the entire IoT & AI value chain — from data extraction to deployment — by using just one platform.

More IoT & AI in the automotive industry: application scenarios by division

Car manufacturers can utilize IoT & AI in R&D, manufacturing and supply chain operations. But AIoT in automotive is beginning to show its effects in somewhat counterintuitive areas such as marketing, sales, and finance where IoT data analytics lead to more automation and higher efficiencies.

Research and Development

The confluence between IoT & AI is bound to breathe fresh life into R&D initiatives, rejuvenating R&D projects and making it possible to fully unfold their potential. IoT & AI will also help predict the success rates of different initiatives and better estimate the time and resources needed for completion. This way, automotive companies can remain on the cutting edge while saving money. Identifying and focusing on high-potential high-feasibility initiatives is where AI can supplement IoT in creative and unconventional ways. 

Manufacturing

AI-driven systems help car manufacturers to create and oversee workflows with more efficiency. Further, they enable the timely identification of defective car parts, produced both in-house or coming from external suppliers, before they are installed in vehicles and headed for the road. The combination of IoT & AI (AIoT) also provides the basis for stress measurement and deformation identification techniques, the development of safety concepts, and predictive maintenance use cases. 

Supply chain operations

Supply chain data analytics have been around for a while. However, supplementing front-end AI systems with IoT technology can make all the difference when it comes to tapping into new data sources by analyzing even larger and more diverse data sets, overseeing logistics on a grand scale, or managing risk. Using classic metrics such as delivery performance and the platform’s collaborative features, automotive companies can achieve more transparency in their supply chain operations, identify typical scenarios for delivery failure, and begin to cooperate more with their suppliers towards a common goal. 

Summing up: The benefits of IoT & AI platforms for automotive

The combined forces of IoT & AI are transforming and will continue to inspire innovation in just about every aspect of the automobile industry. Untapping those potentials and identifying revenue opportunities with IoT & AI can be the ultimate game-changers for automotive manufacturers, suppliers, and innovators. 

Summing up, the confluence of IoT & AI can optimize automotive production across the entire value chain. Further, IoT & AI help you increase car safety and drive customer loyalty. We all know about the possibilities opened up by autonomous and semi-autonomous driving, in-vehicle speech recognition systems based on Natural Language Processing (NLP), computer vision, deep learning algorithms that improve navigation, or automaker AI solutions for car safety management such as object detection and course calculation. The possibilities are endless:

Innovative business models and revenue streams 

IoT&AI-driven innovations can help you tap into new and unexpected revenue sources. Further, you can develop yet-unseen business models around digital products developed on a combined IoT & AI platform.

Cost reduction

Supplementing IoT with AI means increased operational efficiency and revenue boosts in all areas of automotive. But often the untold story is that of all the cost cuts and savings that take place thanks to the implementation of combined IoT & AI solutions. This involves resource savings thanks to automation, operational streamlining, digital assistants, and more. 

Risk reduction

Predictive maintenance, anomaly detection, smart object recognition and built-in AI-powered security systems play an increasingly crucial role in developing innovative models around vehicle safety. Implementing viable risk reduction scenarios is especially relevant in emerging areas such as autonomous or semi-autonomous driving. Across all these domains, IoT&AI-driven innovation is key in developing concepts for safer and stress-free driving.

Enhanced customer experience

Customized IoT&AI technology can help engage with customers in a sustainable way. You boost customer loyalty and create overwhelmingly positive customer experiences thanks to tailored digital services. Data-driven manufacturers are better equipped to develop customer retention solutions. You manage long-term relationships by identifying customer churn in advance and developing targeted offers to counteract unwanted developments.

Using real data, we test use cases quickly and help you seamlessly transition to the PoC and prototype phases. Get in touch to learn more about the Record Evolution platform for IoT & AI. Find out how you can utilize the platform to build your own digital solutions.

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Marko Petzold
Record Evolution GmbH
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