Common Uses of Cloud Data Services in the AGV Market

The global automated guided vehicle market size was valued at USD 3.0 billion in 2019 and is expected to experience a CAGR of 14.1% from 2020 to 2027. Regardless of the application, AGV presents an interesting challenge for Edge computing, modern cloud infrastructure and data storage systems.

The next three years will see an explosive growth in connected cars; and this year, 2020, it is expected that 98% of all new cars being sold will be connected. The growth is fueled by ubiquitous mobile networks plus government-required connectivity.

Autonomous vehicles will have to perform many more functions beyond driving, performing functions such as correctly recognizing passenger identities, setting child locks for younger passengers, assisting physically impaired passengers, as well as handling a multitude of navigational and safety-critical data in real time. The majority of data processing will be on the edge, while deep learning will be in the cloud.

As with most other services, owners and drivers will want to have control over the use of their data and many governments already mandate such consumer control. For example, car owners and drivers may choose to share some of the telemetry data with insurance companies in order to obtain discounts, to participate in studies, or comply with laws. Let’s look at a few of the more common use case today.

Test Environments

As we look at the early stages of data collection from AGVs, as you might surmise, much of the data is coming from test environments. OEMs and tech companies continue to develop autonomous vehicles, and are subjecting driverless prototypes to stringent and repeated testing across multiple geographies and physical environments. Deep learning and prototype validation will come from exploiting the data life cycle: collecting, analyzing, storing, processing and archiving data for attributes including vehicle location, road and weather conditions, anomalies and more. For now, all data will need to be collected and analyzed; however, the challenge of data collection, transfer and analysis requires attention to connectivity, compute and storage infrastructure. This is where the careful selection of a cloud provider and cloud data services is essential.

As we look to the future, in addition to test environments, data will be collected for such things as fleet management and predictive maintenance.

Fleet Management

Although fleets can range in size from just a handful of vans for a small business, to thousands of big rigs operated by a long-haul transporter, all fleet operators face the same challenges: Where is my fleet right now? Where are the vehicles needed? How can we optimize fleet operations to improve productivity and reduce costs?

Today, IoT sensors embedded in fleet vehicles are enabling fleet managers to capture and analyze data via predictive analytics for driver behavior, maintenance, fuel efficiency, route planning and more. Incorporating IoT for any size fleet requires connectivity to a software platform and a data architecture that can handle large quantities of data generated from many endpoints. This data needs to be aggregated, processed and stored so that actionable insights can be gained from deeper learning in the cloud. Interconnection that links IoT assets with multiple big data platforms and data users is becoming critical to smart fleet management. The ability to provide high speed data transfer across a vast network with agile and secure, interconnection brings commercially viable, high-power processing capabilities to the autotech ecosystem.

Predictive Maintenance

Connectivity is increasingly part of vehicle design for a plethora of reasons including vehicle management, safety, navigation, autonomous driving, communication, and entertainment. Back in 2017 between 60% and 80% of cars sold included OEM-installed telematics, which combine IoT with machine learning/AI for condition monitoring and predictive maintenance. Multiple sensors in these vehicles monitor engine performance, transmission and exhaust system conditions, tire pressure and oil levels. Looking ahead, sensors will need to evolve considerably to support predictive maintenance and will need to coordinate with smart systems that track weather conditions and vehicle location. Sensor and system data are aggregated and transmitted for analysis by a service provider, which can then trigger appropriate follow-up steps.

Condition monitoring and predictive maintenance can help both consumers and fleet managers avoid unplanned vehicle downtime as well as offer insights into parts usage and wear for such things as brakes and tires. Condition monitoring has the potential to generate 600 MB per day per car. As more vehicles are delivered with condition monitoring and predictive maintenance capabilities, the large quantities of data produced in real time will require that OEMs proactively manage the data life cycle to unlock value and leverage opportunities.

As we prepare for the coming era of autonomous guided vehicles, OEMs should start their data process by listing which data types must be collected, aggregated and ingested, and seek to ensure compliance with privacy laws throughout the life cycle. Data value increases as it is analyzed and enriched with other data, creating predictive insights. The combination of sensor and system data enables valuable insight into the time left before the car part will fail. When insights from hundreds or thousands of individual vehicles are aggregated and synthesized with high-performance computing capabilities in a hyperscaled cloud or colocation environment, the OEMs can monetize these insights, sharing them with parts manufacturers, insurance companies and others in the ecosystem.

To learn more about how you can gain efficiencies with cloud data services contact us.

You may also like…

A Bold New Look for RSTOR

A Bold New Look for RSTOR

Over the coming weeks and months, you’ll start to see some visual and cosmetic changes at RSTOR and I want to take a few minutes to explain what is happening and why.

Accelerating Insights to Action – Part V

Accelerating Insights to Action – Part V

Speed to insight has long been an important objective, but users are often frustrated by delays in getting from data to insights, sometimes due not to technology but rather to project issues. Challenges are only growing as data, especially streaming data, gets more voluminous, varied, and moves with greater velocity. Fortunately, cloud vendors are providing solutions and options to fit ambitious use cases as well as corresponding analytics and AI workloads.

Accelerating Insights to Action – Part IV

Accelerating Insights to Action – Part IV

For project teams to attract investment in technologies and cloud services to enable faster data and support faster analytics, it is essential to articulate carefully the benefits that would accrue to the organization.