How AI and Cloud Technology are Transforming Fleets

The ability of AI to analyze large amounts of information from telematics devices provides managers with valuable information to improve fleet efficiency, reduce costs and optimize productivity.

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As fleets in the supply chain look to modernize the way they maintain their fleet of business vehicles, the benefits and use of connected vehicles could continue to define a new set of standards. 86% of connected fleet operators recently surveyed have reported a solid return on their investment in connected fleet technology within one year through reduced operational costs.

Additionally, an increasing number of supply chain fleets are realizing that connected vehicles with sophisticated telematics are offering even more benefits in managing vehicle performance and activity. Another study illustrated a 13% reduction in fuel costs for surveyed businesses, along with improvements to preventive maintenance. It also showed a 40% reduction in harsh braking, showing modifications to driving habits that could both contribute to parts longevity and improve driver safety.

Large amounts of data are difficult to process

Of course, fleets, insurance companies, maintenance and aftersales companies alike are increasingly looking to this intelligent telematics data. However, the amount of data generated every day is increasing. As a result, these companies have more data than ever to make informed business decisions. However, this massive amount of data introduces many new challenges to efficiently capture, process and analyze all this data.

To truly be effective and useful, data must be tracked, managed, cleansed, secured and enriched throughout its journey to generate the right insights. Insurers working with automotive fleets are turning to new processing capabilities to manage and make sense of this data.

Embedded systems technology has been the norm

Existing supply chain companies for their business fleets have relied on embedded systems - devices designed to access, collect, analyze (in-vehicle) and monitor data from electronic equipment - to solve a variety of problems. These embedded systems are particularly popular in consumer electronics and are increasingly used today in vehicle data analysis technology.

Why current solutions are not very efficient

The existing solution in the market is to use the low latency of 5G. Using AI and GPU acceleration on AWS Wavelength or Azure Edge Zone, vehicle OEMs can offload onboard vehicle processors to the cloud when feasible. This approach allows traffic between 5G devices and content or application servers hosted in Wavelength zones to bypass the internet, resulting in reduced variability and content loss. 

To ensure optimum accuracy and richness of datasets, and to maximize usability, sensors embedded within the vehicles are used to collect the data and transmit it wirelessly, between vehicles and a central cloud authority, in near real-time. Depending on the use cases that are increasingly becoming real-time oriented such as roadside assistance, ADAS and active driver score and vehicle score reporting, the need for lower latency and high throughput have become much larger in focus for fleets, insurers and other companies leveraging the data.

However, while 5G solves this to a large extent, the cost incurred for the volume of this data being collected and transmitted to the cloud remains cost prohibitive. This makes it imperative to identify advanced embedded compute capability inside the car for edge processing to happen as efficiently as possible.

The rise of vehicle to cloud communication

To increase the bandwidth efficiency and mitigate latency issues, it’s better to conduct the critical data processing at the edge within the vehicle and only share event-related information to the cloud. In-vehicle edge computing has become critical to ensure that connected vehicles can function at scale, due to the applications and data being closer to the source, providing a quicker turnaround and drastically improves the system’s performance.

Technological advancements have made it possible for automotive embedded systems to communicate with sensors, within the vehicle as well as the cloud server, in an effective and efficient manner. Leveraging a distributed computing environment that optimizes data exchange as well as data storage, automotive IoT improves response times and saves bandwidth for a swift data experience. Integrating this architecture with a cloud-based platform further helps to create a robust, end-to-end communications system for cost-effective business decisions and efficient operations. Collectively, the edge cloud and embedded intelligence duo connect the edge devices (sensors embedded within the vehicle) to the IT infrastructure to make way for a new range of user-centric applications based on real-world environments.

This has a wide range of applications across verticals where resulting insights can be consumed and monetized by the OEMs. The most obvious use case is for aftermarket and vehicle maintenance where effective algorithms can analyze the health of the vehicle in near real-time to suggest remedies for impending vehicle failures across vehicle assets like engine, oil, battery, tires and so on. Fleets leveraging this data can have maintenance teams ready to perform service on a vehicle that returns in a far more efficient manner since much of the diagnostic work has been performed in real time.

Additionally, insurance and extended warranties can benefit by providing active driver behavior analysis so that training modules can be drawn up specific to individual driver needs based on actual driving behavior history and analysis. For fleets, the active monitoring of both the vehicle and driver scores can enable reduced TCO (total cost of ownership) for fleet operators to reduce losses owing to pilferage, theft and negligence while again providing active training to the drivers.

Powering the future of fleet management

AI-powered analytics leveraging IoT, edge computing and the cloud are rapidly changing how fleet management is performed, making it more efficient and effective than ever. The ability of AI to analyze large amounts of information from telematics devices provides managers with valuable information to improve fleet efficiency, reduce costs and optimize productivity. From real-time analytics to driver safety management, AI is already changing the way fleets are managed.

The more datasets AI collects with OEM processing via the cloud, the better predictions it can make. This means safer, more intuitive automated vehicles in the future with more accurate routes and better real-time vehicle diagnostics.