8 Auto Industry Challenges You Can Solve With AWS Automotive Solutions [+ Real Examples]

AWS offers a generous array of services, purpose-built for the automotive industry. This makes it easier to handle data, increase compute power, manage storage, conduct batch analytics, and build machine learning (ML) models. By choosing the right services for your business, you can develop more scalable, secure, and cost-efficient solutions. 

Here, we explore the different challenges the automotive industry faces that can be solved with AWS solutions.

Table of contents

• 8 Automotive industry challenges solved with AWS solutions

• Examples of using AWS for scalable automotive solutions

• The bottom line: What AWS automotive solutions are right for my needs?

8 Automotive industry challenges solved with AWS solutions

Modern vehicles need to be smart and safe. To do so, automobile manufacturers build processes that leverage ML models and improve systems continuously. 

However, developing this sort of software is complex and expensive. It requires large processing and computing power at a scale—and on the cloud. That’s why, Amazon offers different AWS services to overcome these common automotive industry challenges:

1. Autonomous mobility

As you know, autonomous vehicles (AV) rely heavily on machine learning models and IoT systems. For these vehicles to drive safely while adhering to local regulations and ensuring the safety of all individuals, they must be built on interconnected systems. This can only happen through cloud-native data engineering solutions.

The challenge lies in accurately building processes for data collection, ingestion, processing, and anonymization before training ML models or running safety simulations. Since this can be expensive and resource-intensive, AWS offers key services to support these technical requirements.

• AWS Direct Connect to ensure your network traffic stays within the AWS network, even while in transit

• Amazon Simple Storage Service (S3) for scalable, available, and secure object storage

• Amazon Elastic Compute Cloud (EC2) to increase compute capacity in a secure cloud environment

• AWS Batch for hassle-free running of batch computing jobs on AWS

• Amazon SageMaker to develop and train ML models

2. Connected mobility

Car manufacturers use sensors to collect and monitor data from millions of connected vehicles. This interconnectivity allows you to track and recall analytics and insights, build connected vehicle platforms, manage entire fleets, design digital twins, and collect vehicle data.

The biggest challenges in this area revolve around interoperability and making sure infotainment, advanced driver-assistance systems (ADAS), and vehicle-to-everything (V2X) work properly.

With those challenges in mind, AWS provides you with solutions for real- or near-real-time data processing, predictive maintenance, and enhanced responses to improve customer satisfaction. Also, Amazon offers serverless computing, making it a cost-efficient solution. Some of these services include:

• AWS IoT FleetWise to collect and store vehicle data to improve its quality and performance

• AWS IoT Core to connect IoT devices and redirect messages to AWS services automatically

• AWS IoT Device Management to enroll, arrange, oversee, and control IoT devices remotely on a large scale

• AWS IoT TwinMaker to develop digital twins of your real-world systems

• AWS Lambda to run code for any software without needing to manage its servers

At NaNLABS, we have experience designing and implementing centralized data warehousing solutions that consolidate structured sensor data from all vehicles. This way, you can perform long-term data analysis and generate insights on vehicle status and health, identify patterns, and predict potential maintenance needs in one place.

Let us drive you toward scalable and connected mobility!

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3. Software-defined vehicles

Software and computers were introduced into the vehicle manufacturing process in the 1960s. Since then, they have become less mechanical and are almost fully software-dependent.

AWS understands what this means in terms of building complex and robust polyglot architectures, scalable infrastructure, and choosing the right cloud services. That’s why its services enable access to 5G/MEC features for development and deployment, secure cloud hosting, preparation for over-the-air (OTA) updates, and scalable cloud testing. 

The most common AWS services to use for developing and deploying your vehicle’s software include:

• AWS CodePipeline to automate your application and infrastructure release pipelines for fast and reliable updates

• AWS CodeCommit as a secure, scalable source control service that hosts private Git repositories 

• AWS CodeBuild to compile code, test it, and deploy software packages for CI

• AWS IoT Device Management to enroll, arrange, oversee, and control IoT devices remotely at a large scale

• AWS IoT Core to connect IoT devices and redirect messages to AWS services automatically

4. Manufacturing

The manufacturing process involves coordinating and automating the work of multiple factory machines and assembly lines. With some machines operating on legacy systems or following different protocols, it’s essential that you effectively collect, store, and analyze the vast inflow of data from them.

Since handling high data volumes is challenging, AWS automotive services offer tailored solutions. These services also allow you to optimize your manufacturing process, reduce costs, and increase productivity.

The most common AWS solutions to use for manufacturing include:

• Amazon RDS to handle relational databases at a scale and on the cloud

• AWS Snowball to handle data migration, edge computing, and edge storage

• Amazon Forecast to analyze business metrics and forecasting by using ML

• AWS IoT SiteWise to recall, arrange, and analyze data coming from industrial equipment

• AWS Private 5G to operate a private cellular network through a fully-managed service

5. Supply chain

Gain real-time insights into your entire supply chain, inventory, and logistics networks. Since you likely work with multiple suppliers, line operators, dealerships, and online retailers, visibility at every step of the process is crucial. This can be challenging to achieve without software that provides real-time information or automated solutions.

As vehicles evolve, supply chains are also transforming. Therefore, you need to use a flexible cloud-based system that meets your needs. To see all your key services in a single-page view, consider using AWS services such as:

• AWS IoT Core to connect devices and redirect messages to other relevant AWS services

• Machine Learning on AWS to analyze your data to drive insights and predictions

• AWS IoT Greengrass to install edge runtime to develop, launch, and handle cloud-based software

• Analytics on AWS to process huge data sets and provide you with analytics

 6. Customer experience and digital engagement

Automotive brands can use AWS to create personalized, engaging digital experiences across the customer journey. You can get better at determining what your customer needs at each touchpoint of their journey by tracking and analyzing customer behavior. Then, crafting and sharing lifestyle-based recommendations.

This is challenging because, without software that provides real-time information or automated solutions, it’s impossible to manage and analyze increasing volumes of customer data. And, not being able to process data on time, limits your ability to deliver personalized services and make data-driven decisions for marketing or product development.

These are some of AWS’s solutions for analyzing customer data to improve customer satisfaction and loyalty:

• Amazon Connect to design and deploy a contact center to offer customer support at a scale

• AWS Lake Formation to simplify setting permissions for data management and enable sharing it internally and externally without putting security at risk

• Amazon Kinesis to collect, store, and analyze data coming from videos in real-time

• Amazon SageMaker to create, train, and deploy ML models

• Amazon Personalize to segment users automatically and trigger personalized recommendations based on shared characteristics at a scale

Along with AWS solutions, the NaNLABS team can help you navigate customer engagement challenges. We can do so by implementing a centralized cloud-native data architecture that consolidates customer behavior data from multiple sources. 

Then, we’ll use a data warehouse for structured data organization and a data lake for managing unstructured information—and give you full visibility over customer information. This approach improves the customer experience by making better decisions based on data at a scale.

Let us take the wheel and increase your customers’ experience.

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7. Product engineering

Building a car today is almost like combining the complexity of a smartphone with the extensive safety requirements of a vehicle. Without the right cloud-native services to support your architecture, it may be steep to design models, develop software, and analyze data efficiently. 

AWS lets you build systems on a secure, powerful, scalable cloud to reduce time-to-market and costs. It also facilitates team collaboration regardless of location. 

Use AWS services to navigate cloud complexities, validate tests, simulate performance measurements, and concentrate on developing vehicles within private or public networks.

Some AWS services that can help you during product engineering include:

• Amazon EC2 to host your systems on a secure cloud with scalable computing

• Amazon S3 for high volume and secure object storage

• Amazon WorkSpaces to support remote collaboration by offering desktop visualization and resource access from different devices

• Amazon FSx for Luster to access scalable storage for compute workloads

• AWS ParallelCluster to launch and handle High-Performance Computing (HPC) clusters on AWS

• Elastic Fabric Adapter (EFA) to operate systems with extensive inter-node communication requirements

8. Predictive maintenance

Vehicles generate massive amounts of data that can help you anticipate future malfunctions. Determining the right processes and tools to collect, organize, store, and analyze this information can significantly impact your ability to predict future maintenance issues. 

Some AWS services you can use for predictive maintenance include:

• Amazon Monitron to diminish unplanned downtime by using ML predictive maintenance 

• Amazon S3 to store and handle massive loads of data

• AWS IoT Core to connect and manage IoT devices, enabling real-time data collection from vehicle sensors

• AWS Lambda to automate processes, such as triggering maintenance alerts based on sensor data thresholds

• Amazon Kinesis to collect, process, and analyze real-time streaming data from connected vehicles for immediate insights

• AWS Glue to handle data integration and ETL (Extract, Transform, Load) processes, preparing data for analytics and machine learning

• Databricks Lakehouse Platform to use a unified data platform for storing, processing, and analyzing structured and unstructured data from vehicle sensors

• Amazon Athena to easily conduct flexible data analysis at a petabyte-scale

Imagine you’re an electric vehicle (EV) manufacturer that’s looking to improve predictive maintenance capabilities. This will allow you to ensure reliability while minimizing downtime for fleet operators. However, your data is fragmented across multiple systems, which makes it difficult to apply AI and ML models.

If you were working with us at NaNLABS, we would use AWS Redshift and Databricks to build a cloud-native data lakehouse. This data lake house would integrate structured data (e.g., sensor telemetry or vehicle diagnostics) with unstructured data (e.g., usage patterns or driver behavior logs). 

A well-organized architecture enables you to develop AI-driven solutions for predictive maintenance and performance optimization—saving both time and money while preparing you for future growth.

Let’s drive together toward better data management to improve your predictive analysis capabilities.

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Examples of using AWS for scalable automotive solutions

Here are two real-life examples of using AWS solutions in the automotive industry: 

AWS Generative AI and Ferrari 

Ferrari, the Italian sports car manufacturer, used AWS Generative AI to streamline the car design process, improve fan engagement, and enhance customer interactions. 

This led to a 20% increase in saved vehicle configurations and better productivity for over 1,000 technical users thanks to an AI-powered knowledge base. 

Also, thanks to Amazon Bedrock, Ferrari managed to access Amazon Titan and Anthropic Claude foundation models. This allowed Ferrari to customize models and run simulations 60% faster.

Overall, AWS's extensive cloud platform continues to allow Ferrari to innovate continuously while improving customer engagement and operational efficiency.

Infrastructure and system development for managing EV charging stations

At NaNLABS, we offer cloud-native data engineering development services for automotive companies. We recently worked with a client that offers EV charging stations for fleet operators with medium to heavy-duty transports. 

This client needed to integrate and process massive volumes of structured and unstructured data. During the collaboration, we: 

• Built a cloud-based charge management system (CMS) to allow fleet managers to book a time to use EV chargers

• Designed and developed a reservation system beyond the CMS, which enabled fleet managers to plan charge sessions within the charging sites

• Built a data lake and developed data pipelines for real-time, near real-time, and batch analytics

• Led load tests using electric vehicle supply equipment (EVSE) simulators to assess software performance across various scenarios.

After these developments, the client was able to get insights from the data coming from IoT devices at all times. This also led to the team getting alerts in case of malfunctions such as getting no data from chargers or having insufficient charging power. 

The bottom line: What AWS automotive solutions are right for my needs?

Navigating the challenges of the automotive industry requires a robust, cloud-native strategy and solutions. With AWS automotive solutions, you can handle the end-to-end of your operation from managing the supply chain at a scale to developing autonomous and connected vehicles and improving the customer experience.

That’s why choosing the right AWS solutions for your business isn’t something that can’t be defined in this (or any) blog post. However, if you’re struggling to define the right tech stack for your vehicle, partnering with an AWS consulting firm like NaNLABS allows you to delegate this selection to experts. Invite NaNLABS to take the co-pilot’s seat and share directions toward a fully cloud-native, efficient, and intelligent future.

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