Cloud computing, which is already invaluable to nearly every aspect of business infrastructure, has also become integral to the development and deployment of software-defined vehicles (SDVs). Amazon Web Services (AWS Cloud), the leading public cloud provider, has been staking out a leadership role in providing the automotive industry with cloud services tailor-made to the needs of automotive, including advanced driver assistance systems (ADAS), autonomous driving, fleet management, AI/ML, and vehicle data management, and many other aspects of connected vehicles. They are providing services ranging from compute, data management, vehicle prototyping, and much more.
The cloud is also important to us: Sonatus delivers cloud software that takes advantage of these capabilities in our products. This blog will explain why the cloud is so critical to automotive companies, and in particular some of the ways AWS is leading connected mobility.
Cloud Computing
One of the most fundamental benefits of cloud computing is the ability to flexibly expand compute capabilities based on variable demand, giving rise to AWS services for compute, especially EC2 (Elastic Compute Cloud) and EKS (Elastic Kubernetes Service). Based on the needs of the business, compute instances can be activated and put to work quickly – nearly instantaneously, if needed – while not consuming capital nor operating costs when not in use. This is an incredibly powerful tool for businesses and has changed the way businesses use AWS cloud .
A second equally-valuable benefit is the incredible diversity of compute instance types that provide a wide range of compute, storage, memory and specialized processing capabilities. Given the growing shift to Arm-based compute in vehicles, AWS infrastructure offers high performance computing instances containing Arm-based AWS’s Graviton processor, enabling “environmental parity” between vehicles and the cloud.
Sonatus products across our Sonatus Vehicle Platform comprise in-vehicle software and corresponding cloud software that is built on and leverages the many diverse AWS solutions, including Kubernetes and containerized workloads, based on both Amazon EC2 and Amazon EKS.
Cloud data management
The amount of data produced by a vehicle connectivity is skyrocketing, making it more and more complicated for the automotive industry to capture, store, upload, and manage. While limited vehicle telemetry and sensor data have been around a while, in a software defined vehicle, data signals are rapidly increasing as more vehicle systems are becoming digital and connected. Adding to that, the proliferation of rich data sources like video cameras, Radar, and LiDAR are expanding, delivering more advanced capabilities but generating significantly more data in the process. Leading-edge automotive OEMS are taking advantage of this data, creating integrated “data lakes” to form a single source of truth that can be queried in different ways from across their organization to add intelligence and ensure consistency in decision making.
Gathering data from in vehicle systems produces a wide range of benefits including unlocking new business models. Here are a tiny fraction of the countless interesting use cases of vehicle data that can benefit from the vehicle connectivity to the cloud:
- Safety monitoring
- Fault diagnostics
- Predictive analytics / predictive maintenance
- Ongoing component optimization
- Efficiency tuning
- Vehicle usage analysis
- Future vehicle planning
- Communication between vehicle and road infrastructure such as traffic lights (V2I)
- Communication between other nearby vehicles (V2V)
Advancing the ability to collect valuable, carefully selected data from vehicles is critical. Sonatus Collector is in production today and creating useful databases for our OEM customers that allow them to solve real problems and add customer value. Collector is a perfect complement to AWS services for data storage that allow scalable storage of different data types to enable a range of automotive solutions.
Vehicle Virtual Prototyping
Another important application of the cloud is rapid prototyping. Developing software for vehicles is challenging given its deeply-embedded environment with multiple interlocking systems. Moreover, time-to-market pressure is pervasive, so it is incredibly beneficial to be able to develop software in parallel with hardware. This is sometimes referred to as “shift left” for allowing software development to come earlier in the cycle.
Offering cloud instances with compatible architectures to vehicles can significantly improve developer efficiency and speed time to market by allowing development in the cloud and deployment later to vehicles. This design style can also promote collaboration across teams in multiple geographies, who may be addressing different regional requirements. In fact, it even allows virtual prototyping and software development while the hardware is still being designed.
The most capable teams use this approach to debug the hardware before it is finished, which has been proven to significantly speed time to market and reduce hardware iteration cycles to fix bugs. This prototyping approach also has additional benefits as OEMs are increasingly shifting their vehicle architectures away from dedicated ECU’s carrying out a single, fixed function to consolidated architectures with multiple cores side by side and managed through hypervisors and virtualization. That design style is more scalable, easier to verify, and matches the expertise of the cloud for prototyping and later for production deployment into vehicles.
Sonatus is using AWS to prototype our own work to increase the number of test cases we can run without being forced to replicate physical hardware or repeatedly power-cycle it to restart a test.
AI / Machine Learning and Data Analytics
Once we bring together a compelling data set and combine it with scalable, flexible compute, it unlocks the ability to do incredible data analytics that would never have been possible before. Add to that the rapid expansion in machine learning (ML) capabilities and even more powerful questions can be asked of data. ML algorithms are continuously evolving and improving, and cloud computing approaches allow rapid training of the models in the cloud. In particular, as the industry works to develop ML-based models for ADAS and Autonomous driving, the cloud model can be incrementally deployed to vehicles with feedback to ensure the new models do not cause regression.
Another of ML’s strengths is the ability to detect patterns and anomalies that would be difficult to code using conventional approaches. By studying data from a large number of vehicles, patterns can emerge that can signal early warning signs. For example, smart data collection coupled with ML can be used to proactively detect anomalies and flag them for corrective action before they become dangerous recalls. Sonatus OEM customers are already using Sonatus Collector to anticipate issues and better respond to service needs for their customers.
ADAS and Autonomous Driving
The advent of ADAS and its higher-capability cousin, autonomous vehicles, requires significant modeling and tuning, and the cloud is critical to these advances and to enable scalable intelligent transport systems. The complexity of these tasks cannot be overstated, especially the higher levels of autonomy, and it is only through cloud computing and incremental model improvements that make the rapid improvements in these capabilities possible across the automotive industry.
First, automotive companies can capture and accumulate data from a connected car over a cellular network to build a database that is representative of real driving tasks to stimulate the recognition and action models. The benefits of this approach do not stop there: most of the time, normal driving is “boring” with few difficult scenarios occurring per drive. In a simulated environment, we can artificially push through corner cases at an accelerated rate and make far faster training progress compared to only training based on vehicles in the field. The Sonatus Vehicle Platform can be used to capture rich data to assist in the important analysis needed for ongoing improvement of ADAS and autonomous driving, adding a new powerful tool to the engineers doing this important work.
At CES 2024, Sonatus and AWS demonstrated an approach to improve road safety via remote vehicle diagnostics, such as when a vehicle failed to detect pedestrians. We demonstrated how a connected vehicle equipped with Sonatus Collector can send targeted vehicle to cloud real time data about the failures. Once in the cloud, AWS can combine multiple data sources in real time, such as high fidelity ML analytics tool Rekognition as well as manufacturing data about the vehicle and its current software and hardware versions. The combined approach can quickly identify the reason for the vehicle failing to issue collision warnings such as a weakness in the pedestrian machine learning models, a problem with camera hardware, or other devices in the vehicle, to quickly resolve the issue.
Learn more from The Garage Podcast
If you found this topic interesting, you can learn a lot more: I recently had a long, two-part conversation with Stefano Marzani, the Worldwide Tech Leader for Software-Defined Vehicles at AWS for our podcast, The Garage. In that wide ranging discussion (links to Part 1 and Part 2), we touch on all of the topics above in incredible detail. If you’d like to learn more about this topic and hear from one of the leading voices in the industry on SDV, don’t miss those two episodes. You can find the full The Garage podcast on YouTube or on Spotify or Apple Podcasts. Be sure to subscribe to be notified of future episodes!