As supply chain fleets 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 reported a solid return on their investment in connected fleet technology within one year through reduced operating costs.
In addition, a growing number of fleets in the supply chain are realizing that connected vehicles with sophisticated telematics offer even more advantages in managing vehicle performance and activity. Another study illustrated a 13% reduction in fuel costs for the companies surveyed, along with improvements in preventive maintenance. It also showed a 40% reduction in hard braking, showing modifications to driving habits that could 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 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 before 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 be truly effective and useful, data must be tracked, managed, cleaned, protected and enriched throughout its journey to generate the right information. Insurers working with car 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 commercial fleets have relied on embedded systems (devices designed to access, collect, analyze (in-vehicle) and control 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. By accelerating AI and GPU in AWS Wavelength or Azure Edge Zone, vehicle OEMs can offload onboard vehicle processors to the cloud when possible. This approach allows traffic between 5G devices and content or application servers hosted in wavelength zones to bypass the Internet, reducing variability and content loss.
To ensure optimal accuracy and richness of data sets, and to maximize usability, sensors embedded in vehicles are used to collect the data and transmit it wirelessly, between vehicles and a central cloud authority, in near real-time. Depending on use cases that are increasingly real-time oriented, such as roadside assistance, ADAS and active driver score and vehicle score reports, the need for lower latency and high performance has been much more focused on the fleets, insurers and other companies that take advantage. the data.
However, while 5G solves this to a large extent, the cost incurred by the volume of this data being collected and transmitted to the cloud remains prohibitive. This makes it imperative to identify advanced built-in computing capability within the car so that edge processing occurs as efficiently as possible.
The increase in communication between vehicles in the cloud
To increase bandwidth efficiency and mitigate latency issues, it is better to perform critical data processing at the edge of the vehicle and share only event-related information to the cloud. Vehicle edge computing has become critical to ensuring that connected vehicles can operate at scale, as applications and data are closer to the source, providing faster change and dramatically improving system performance.
Advances in technology have made it possible for embedded automotive systems to communicate with sensors, both inside the vehicle and on the cloud server, in an effective and efficient manner. Leveraging a distributed computing environment that optimizes data sharing and data storage, automotive IoT improves response times and saves bandwidth for a fast data experience. Integrating this architecture with a cloud-based platform further helps 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 edge devices (sensors embedded within the vehicle) to the IT infrastructure to enable a new range of user-centric applications based on in real world environments.
This has a wide range of applications across verticals where OEMs can consume and monetize the resulting insights. The most obvious use case is for the aftermarket and vehicle maintenance, where effective algorithms can analyze vehicle health in near real-time to suggest remedies for impending vehicle failures in vehicle assets such as the engine, oil, battery, tires, etc. Fleets that take advantage of this data can have maintenance teams ready to perform service on a returning vehicle in a much more efficient manner, as much of the diagnostic work has been done in real time.
In addition, insurance and extended warranties can benefit by providing active analysis of driver behavior so that specific training modules can be tailored to individual driver needs based on actual history and analysis of driving behavior. For fleets, active monitoring of both vehicle and driver ratings can enable a reduction in TCO (total cost of ownership) for fleet operators to reduce losses due to theft, theft and neglect while providing training enable the drivers.
Driving the future of fleet management
AI-powered analytics leveraging IoT, edge computing and the cloud are rapidly changing the way fleet management is done, making it more efficient and effective than ever before. 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 the AI collects with OEM processing through the cloud, the better predictions it can make. This means safer and more intuitive automated vehicles in the future with more accurate routes and better real-time vehicle diagnostics.
