As construction companies look to modernize their vehicles, the benefits of connected vehicles could make these technologies the new standard for fleet management. In fact, 86 percent of connected fleet operators surveyed by Verizon Connect in 2021 reported a positive 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 managing and maintaining vehicles. Another study by automotive operations company Motive showed a 13 per cent reduction in fuel costs for surveyed businesses, along with improvements to preventive maintenance. It also presented a 40 per cent reduction in harsh braking, showing modifications to driving habits that contribute to parts longevity and improve driver safety.
Large amounts of data are difficult to process
Construction vehicle fleets, their insurance providers, maintenance and aftermarket companies are all looking to harness more of this intelligent telematics data. However, the amount of data produced every day keeps growing. As a result, these businesses have more information to help make better decisions. However, this vast amount of data brings in plenty of new challenges in capturing, digesting and analyzing the entirety of the data 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 correct insights. Companies with construction 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 today, the technology is growing to analyze vehicle data.
Why current solutions are not very efficient
The existing solution in the market is to use the low latency of 5G. Using artificial intelligence (AI) and graphics processing unit acceleration on AWS Wavelength or Azure Edge Zone, vehicle original equipment manufacturers (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.
Using AI and graphics processing unit acceleration on AWS Wavelength or Azure Edge Zone, vehicle OEMs can offload onboard vehicle processors to the cloud when feasible.
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, almost in real time. Depending on the use cases that are increasingly becoming real-time oriented, such as roadside assistance, advanced driver-assistance system and active driver score and vehicle score reporting, the need for lower latency and high output 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 bandwidth efficiency and mitigate latency issues, it’s better to conduct 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 applications and data being closer to the source, providing a quicker turnaround and drastically improving the system’s performance.
Technological advancements have made it possible for automotive embedded systems to communicate with sensors, within the vehicle and the cloud server, in an effective and efficient manner. Leveraging a distributed computing environment that optimizes data exchange, as well as data storage, automotive internet of things (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 information technology infrastructure to make way for a new range of user-centric applications based on real-world environments.
Technological advancements have made it possible for automotive embedded systems to communicate with sensors, within the vehicle and the cloud server, in an effective and efficient manner.
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 almost immediately to suggest solutions for impending vehicle failures across vehicle assets like engines, oil, battery, tires and so on. Fleets leveraging this data can have maintenance teams ready to perform service on a vehicle 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 behaviour analysis so that training modules can be drawn up specific to individual driver needs based on actual driving behaviour history and analysis. For fleets, active monitoring of both the vehicle and driver scores can reduce the total cost of ownership for fleet operators to reduce losses owing to pilferage, theft and negligence while providing active training to 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. AI’s ability 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. 
Sumit Chauhan is co-founder and chief operating officer of CerebrumX, with more than 24 years of experience in automotive, IoT, telecoms and health care. Chauhan has always played the leadership role that allowed him to manage a profit and loss statement of close to US$500,000 across various organizations, such as Aricent, Nokia and Harman, enriching their domestic and international business verticals. As co-founder of CerebrumX, he has applied his experience in the connected vehicle data domain to deliver the automotive industry with an AI-powered augmented deep learning platform. Chauhan is also passionate about mentoring and guiding the next generation of entrepreneurs.
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