How artificial intelligence, edge computing, IoT and the cloud are reshaping fleet management

Utilize a distributed computing environment that optimizes data exchange and data storage to save bandwidth for a fast data experience.

The advantages of connected cars may become the new standard when it comes to fleet management, especially as businesses look to modernize their vehicles. In fact, 86% of connected fleet operators say their investment in connected fleet technology has achieved a significant return on investment within one year through reduced operating costs.

Connected fleets use advanced telematics technology to provide additional advantages for the management and maintenance of vehicles. Another study showed a 13% reduction in fuel costs while improving preventive maintenance. It also showed a 40% reduction in emergency braking, suggesting that changing driving habits can both help extend the life of components and improve driver safety.

A large amount of data is difficult to handle

Fleets, insurance companies and after-sales maintenance companies are eager to utilize more intelligent telematics data. However, the amount of data generated continues to grow. As a result, these businesses have more data than ever to help make informed business decisions. Dealing with so much data creates new challenges to capture, digest and analyze all the information in a cost-effective way.

To generate the right insights, data must be tracked, managed, cleaned, protected and enriched throughout the process to make it truly effective and useful. As a result, those with fleets of vehicles are looking for new solutions to process and understand this data.

Embedded system technology has become the norm

Traditional telematics systems rely on embedded systems that are designed to access, collect, analyze (onboard) and control data from electronic devices to solve a series of problems. Embedded systems are already widely used, especially in home appliances, and the trend of using this technology to analyze vehicle data is growing.

Existing solutions on the market take advantage of the low latency of 5G. Using AI and GPU acceleration on AWS Wavelength or Azure Edge Zone, automotive OEMs can offload automotive processors to the cloud where feasible. This approach enables traffic between 5G devices and content or application servers hosted in the wavelength region to bypass the Internet, reducing variability and content loss.

To ensure optimal accuracy and richness of the data set and maximize usability, sensors embedded in the vehicle are used to collect the data and transmit it wirelessly between the vehicle and a central cloud agency, All of this is done in near real time. With a growing number of real-time oriented use cases such as roadside assistance, ADAS, active driver scoring and vehicle scoring reporting, low latency and high throughput are required for fleets, insurance companies and other businesses leveraging data become more and more important. Although 5G solves this problem to a large extent, the cost of transmitting this data to the cloud is still prohibitive. To maximize edge processing efficiency, advanced embedded computing capabilities must be identified within the car.

The rise of vehicle-to-cloud communications

To improve bandwidth efficiency and mitigate latency issues, it is best to do critical data processing at the edge (within the vehicle) and only share information relevant to the event to the cloud. In-vehicle edge computing is critical to ensuring connected vehicles can operate at scale, as applications and data are closer to the source, providing faster turnaround times and significantly improving system performance.

Agile technological progress has enabled effective and efficient communication between automotive embedded systems and sensors and cloud servers in the vehicle. Leveraging a distributed computing environment that optimizes data exchange and data storage, Automotive IoT improves response times and saves bandwidth for a fast data experience. Integrating this architecture with cloud-based platforms further helps in creating a robust end-to-end communication system for cost-effective business decisions and efficient operations. Overall, edge/cloud and embedded intelligence connect edge devices (sensors embedded in vehicles) to IT infrastructure, making way for a range of new user-centric applications based on real-world environments.

This technology has a wide range of applications across verticals, and OEMs can benefit by leveraging the insights gained from it. The most obvious use cases are aftermarket and vehicle maintenance, where efficient algorithms can analyze the health of a vehicle in near real-time to suggest remedial measures for impending vehicle failures in vehicle assets such as engine, oil, battery, tires, etc. Since most diagnostic work is performed on the fly, fleets can use this data to enable maintenance teams to maintain vehicles in a more efficient manner.

Additionally, insurance and extended warranties can benefit by providing proactive driver behavior analysis so that training modules tailored to individual driver needs can be created based on actual driving history and analysis. For fleets, proactively monitoring vehicle and driver ratings can lower a fleet operator’s TCO (total cost of ownership) by reducing losses due to theft and negligence while providing proactive training for drivers.

Powering the future of fleet management

AI analytics leveraging the Internet of Things, edge computing and the cloud are rapidly changing the way fleet management is performed, making it more efficient and effective than ever before. 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. The way fleets are managed is being changed by the intervention of artificial intelligence, covering all aspects from real-time analysis to driver safety management.

Artificial intelligence can increase the number of data sets OEMs process by collecting them in the cloud, thereby improving their predictive capabilities. This means that future self-driving cars will be safer and easier to use, with more precise routes and better real-time vehicle diagnosis.

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