This industry perspective was written by Sumit Chauhan, co-founder and chief operating officer of Cerebrum X
As telecom companies look to modernize their vehicles, the benefits of connected vehicles could make these technologies the new standard for fleet management. Indeed, 86% of connected fleet operators have already been interviewed reported a solid return on investment in connected fleet technology within a year due to reduced operating costs.
Furthermore, connected fleets with advanced telematics technology now offer further advantages in terms of vehicle management and maintenance. Another study illustrated a 13% reduction in fuel costs for companies surveyed, along with improvements in preventative maintenance. It also showed a 40% reduction in hard braking, showing changes to driving habits that could both contribute to parts longevity and improve driver safety.
Large amounts of data are difficult to process
This means that telecom vehicle fleets, their insurance providers, maintenance and aftermarket companies are all looking to harness more of this smart telematics data. However, the amount of data produced every day continues to grow. As a result, these companies have more data than ever before to make informed business decisions. But this vast amount of data brings many new challenges in cost-effectively acquiring, digesting and analyzing the totality of data.
To be truly effective and useful, data must be tracked, managed, cleaned, protected, and enriched throughout its journey to generate the right insights. Telecommunications companies with fleets of vehicles are turning to new computing capabilities to manage and make sense of this data.
Embedded systems technology has been the norm
Traditional telematics systems have relied on embedded systems, which are devices designed to access, collect, analyze (in-vehicle) and control data in electronic equipment, to solve a variety of problems. These integrated systems have been widely used, especially in home appliances and today the technology is growing in the use of vehicle data analysis.
Because current solutions are not very efficient
The existing solution on 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 whenever possible. This approach allows traffic between 5G devices and content or application servers hosted in Wavelength Zones to bypass the Internet, resulting in less variability and content loss.
To ensure the optimal accuracy and richness of the datasets 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 time real. Depending on use cases that are becoming increasingly real-time oriented, such as roadside assistance, ADAS, and Active Driver Score and Vehicle Score reporting, the need for lower latency and high throughput is become much more important to fleets, insurers and other companies that exploit data.
However, while 5G solves this problem to a large extent, the cost incurred by the volume of this data collected and transmitted to the cloud remains prohibitive. This makes it imperative to identify advanced embedded processing capability within the car for edge processing to occur as efficiently as possible.
The rise of vehicle-to-cloud communication
To increase bandwidth efficiency and mitigate latency issues, it is best to conduct critical data processing at the in-vehicle edge and only share event-related information in the cloud. In-vehicle edge computing has become critical to ensuring connected vehicles can operate at scale, as applications and data are closer to the source, providing faster turnaround and dramatically improving systems performance.
Advances in technology have enabled automotive embedded systems to communicate with sensors, in-vehicle, and the cloud server effectively and efficiently. By leveraging a distributed computing environment that optimizes data exchange and 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 convenient business decisions and efficient operations. Collectively, the edge cloud and embedded intelligence connect edge devices (sensors embedded inside the vehicle) to the IT infrastructure to make way for a new range of user-centric applications based on real environments.
This has a wide range of applications across verticals where the resulting information can be consumed and monetized by OEMs. The most obvious use case is for vehicle aftermarket and maintenance, where effective algorithms can analyze vehicle health in near real-time to suggest remedies for impending vehicle failures through vehicle assets such as engine, oil, battery, tires and so on. Fleets that leverage this data may have maintenance teams ready to service a returning vehicle much more efficiently since much of the diagnostic work was done in real-time.
In addition, insurance and extended warranties can benefit by providing active analysis of driver behavior so that training modules can be designed specifically for the needs of individual drivers based on driver behavior history and analysis. effective guide. For fleets, actively monitoring vehicle and driver scores can enable fleet operators to reduce losses from theft, 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 the way fleet management is done, making it more efficient and effective than ever before. The ability of artificial intelligence to analyze vast amounts of information from telematics devices provides managers with valuable insights 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 computing via the cloud, the better predictions it can make. This means safer and more intuitive automated vehicles in the future with more accurate routing and better real-time vehicle diagnostics.
About the author: Sumit Chauhan is co-founder and chief operating officer of Cerebrum X, with over 24 years of experience in the automotive, IoT, telecommunications and healthcare industries. Sumit has always played the leadership role that enabled him to manage nearly US$0.5 billion P&L in various organizations, such as Aricent, Nokia and Harman, enriching their domestic and international vertical markets. As co-founder of CerebrumX, he applied his expertise in the connected vehicle data domain to provide the automotive industry with an AI-powered augmented deep learning (ADLP) platform. Sumit is also passionate about mentoring and guiding the next generation of entrepreneurs.
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Categories: Artificial Intelligence · Cloud Computing · Industrial Point of View · IoT, M2M
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