As firms look to modernize their vehicles, the advantages of connected vehicles could make these technologies the brand new standard for fleet management. In actual fact, 86% of connected fleet operators already surveyed have reported a solid return on their investment in connected fleet technology inside one 12 months through reduced operational costs.

Moreover, connected fleets with advanced telematics technology today offer additional advantages by way of managing and maintaining vehicles. One other study illustrated a 13% reduction in fuel costs for surveyed businesses, together with improvements to preventive maintenance. It also showed a 40% reduction in harsh braking, showing modifications to driving habits that might each contribute to parts longevity and improve driver safety.

Large amounts of knowledge are difficult to process

This implies vehicle fleets, insurance providers, maintenance and aftermarket firms are all seeking to harness more of this intelligent telematics data. Nevertheless, the quantity of knowledge produced every single day keeps growing. Because of this, these businesses have more data than ever at their disposal to assist make informed business decisions. But, this vast amount of knowledge brings in plenty of recent challenges in capturing, digesting and analyzing everything of the info in an economical manner.

To actually be effective and useful, data have to be tracked, managed, cleansed, secured, and enriched throughout its journey to generate the appropriate insights. Corporations with automotive fleets are turning to recent processing capabilities to administer and make sense of this data.

Embedded systems technology has been the norm

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

Why current solutions will not be very efficient

The prevailing solution out there is to make use of 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 web, leading to reduced variability and content loss.

To make sure optimum accuracy and richness of datasets, and to maximise usability, sensors embedded throughout the vehicles are used to gather the info and transmit it wirelessly, between vehicles and a central cloud authority, in near real-time. Depending on the use cases which are increasingly becoming real-time oriented akin to roadside assistance, ADAS and lively driver rating and vehicle rating reporting, the necessity for lower latency and high throughput have grow to be much larger in focus for fleets, insurers and other firms leveraging the info.

Nevertheless, while 5G solves this to a big extent, the price incurred for the amount of this data being collected and transmitted to the cloud stays cost prohibitive. This makes it imperative to discover advanced embedded compute capability contained in the automobile for edge processing to occur as efficiently as possible.

The rise of car to cloud communication

To extend the bandwidth efficiency and mitigate latency issues, it’s higher to conduct the critical data processing at the sting throughout the vehicle and only share event-related information to the cloud. In-vehicle edge computing has grow to be critical to make sure that connected vehicles can function at scale, on account of 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 speak with sensors, throughout the vehicle in addition to the cloud server, in an efficient and efficient manner. Leveraging a distributed computing environment that optimizes data exchange in addition to 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 sturdy, end-to-end communications system for cost-effective business decisions and efficient operations. Collectively, the sting cloud and embedded intelligence duo connect the sting devices (sensors embedded throughout the vehicle) to the IT infrastructure to make way for a recent range of user-centric applications based on real-world environments.

This has a wide selection of applications across verticals where resulting insights could be consumed and monetized by the OEMs. Essentially 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 forth. Fleets leveraging this data can have maintenance teams able to perform service on a vehicle that returns in a much more efficient manner since much of the diagnostic work has been performed in real time.

Moreover, insurance and prolonged warranties can profit by providing lively driver behavior evaluation in order that training modules could be drawn up specific to individual driver needs based on actual driving behavior history and evaluation. For fleets, the lively monitoring of each the vehicle and driver scores can enable reduced TCO (total cost of ownership) for fleet operators to cut back losses owing to pilferage, theft and negligence while again providing lively training to the drivers.

Powering the long run 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 flexibility of AI to investigate large amounts of data from telematics devices provides managers with worthwhile information to enhance fleet efficiency, reduce costs and optimize productivity. From real-time analytics to driver safety management, AI is already changing the best way fleets are managed.

The more datasets AI collects with OEM processing via the cloud, the higher predictions it may possibly make. This implies safer, more intuitive automated vehicles in the long run with more accurate routes and higher real-time vehicle diagnostics.