How AI, edge computing, IoT and the cloud are reshaping vehicle-fleet management

By Sumit Chauhan, co-founder and chief operating officer of Cerebrum X 

As companies look to modernize their vehicles, the benefits of connected vehicles could make them the new standard, with particular benefits related to fleet management. In fact, 86% of connected-fleet operators reported a solid return on their investment in connected-fleet technology within one year through reduced operational costs. 

Furthermore, connected fleets with advanced telematics technology offer additional benefits in terms of managing and maintaining vehicles. Another study illustrated a 13% reduction in fuel costs, along with improvements to preventive maintenance. It also showed a 40% reduction in harsh braking, indicating modifications to driving habits that could both contribute to parts longevity and improve driver safety. 

Large amounts of data are difficult to process 

Vehicle fleets, insurance providers, maintenance and aftermarket companies are all looking to harness more of this intelligent telematics data. However, the amount of data generated keeps growing. As a result, these businesses have more data than ever at their disposal to help make informed business decisions. But, this vast amount of data brings in new challenges in capturing, digesting and analyzing the entirety of the information in a cost-effective manner.  

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

Embedded-systems technology has been the norm 

Traditional telematics systems have relied upon embedded systems, which are devices designed to access, collect, analyze (in-vehicle), and control data in electronic equipment to solve a set of problems. These embedded systems have been widely used, especially in household appliances, and the technology is growing in the use of analyzing vehicle data. 

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  vehicle processors to the cloud, when feasible. This approach enables 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, all in near real-time. Depending on the use cases that are increasingly becoming real-time oriented—think roadside assistance, ADAS and active-driver score and vehicle-score reporting—the need for lower latency and high throughput have become more important 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 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 improving system 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 vehicles 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 specific to individual driver needs can be created, based on actual driving 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.