The revolution of Artificial Intelligence, Edge Computing, IoT and the Cloud in vehicle fleet management
Over time, companies have continually sought to modernize their vehicle fleets. The benefits emerging from connected vehicle fleets have made these technologies the new standard for fleet management. In fact, more than 80% of connected fleet managers have already reported a high return on their investment in connected fleet technology within the first year due to reduced costs. operatives.
Additionally, fleets linked with advanced telematics technology provide additional benefits in terms of vehicle management and maintenance. A recent analysis demonstrated a 13% decrease in fuel costs for the companies surveyed, along with improvements in maintenance prevention. The analysis also indicated a 40% decrease in the amount of sudden braking, signifying the change in driving habits that can help preserve vehicle parts and improve driver safety.
The challenge of processing large volumes of data
Vehicle fleets, insurance providers, and maintenance and after-sales companies are trying to make the most of this intelligent telematics data. However, the amount of data produced continues to grow and with it, the challenges of capturing, managing and analyzing the torrent of information generated daily.
To optimize data and provide true value, data tracking, management, cleansing, security, and enrichment must be constantly addressed. Because of this, companies with fleets of vehicles are turning to new processing capabilities to handle and make sense of the deluge of data.
The widespread use of embedded systems technology
Traditional telematics systems are based on embedded systems, these are devices designed to collect, analyze (inside the vehicle) and control data on electronic equipment, to find solutions to a variety of problems. These embedded systems have been massively adopted, particularly in household appliances, and today their use is growing in vehicle data analysis.
Why current solutions are not very efficient
The solution currently on the market is the use of the low latency of 5G. By using AI and GPU acceleration on platforms like AWS Wavelength or Azure Edge Zone, vehicle original equipment manufacturers (OEMs) can offload vehicles' onboard processors to the cloud if needed. In this way, traffic between 5G devices and content or application servers hosted in Wavelength zones can bypass the Internet, reducing variability and data loss.
Similarly, to ensure optimal accuracy and enrichment of the data sets and maximize usability, in-vehicle sensors are used to collect the data and transmit it wirelessly, between the vehicles and the central authority in the cloud, almost in real time. Recall that depending on use cases that are becoming increasingly real-time oriented, such as roadside assistance, ADAS, active driver scoring and vehicle score reporting, the need for lower latency and performance high becomes very relevant to fleets, insurers and other companies that use the data.
Unfortunately, although 5G solves this issue to a large extent, the cost generated by the amount of this data that is collected and transmitted to the cloud is still restrictive. This situation highlights the importance of identifying advanced processing capability embedded within the car so that data processing is done as efficiently as possible.
The growth of vehicle to cloud communication
To optimize the efficiency of bandwidth and mitigate latency issues, it is preferable to perform critical data processing at the edge inside the vehicle and only share event-related information in the cloud. Edge computing in vehicles has become critical to ensuring connected vehicles can operate at scale. This is because applications and data are closer to the source, which provides faster response and significantly improves system performance.
Advances in technology have allowed on-board vehicle systems to communicate with sensors, both within the vehicle and the cloud server, effectively and efficiently. Leveraging a distributed computing environment that optimizes data sharing and storage, automotive IoT improves response times and saves bandwidth for a fast data experience. Integrating such architecture with a cloud-based platform further helps create a robust end-to-end communications system for profitable business decisions and operaefficient tions. In fact, the combination of edge computing and embedded intelligence connects edge devices (sensors embedded inside the vehicle) to IT infrastructure to give rise to a new range of user-centric applications in real-world environments.
The applications are varied and cover various business sectors where OEMs can make profitable and profitable use of the information obtained. A dominant use case is vehicle maintenance and aftermarket, where intelligent algorithms can analyze vehicle status in real time to suggest diagnostics and solutions related to possible vehicle failures in components such as engine, oil, battery , tires, etc.
In this context, fleets that take advantage of this data can have maintenance teams prepared to care for a returning vehicle in a much more efficient way, since much of the diagnosis has been carried out in real time.
Additionally, the insurance and extended warranty industry can benefit by providing active analysis of driver behavior. In this way, specific training programs can be developed for each driver based on history and analysis of actual driving behavior. For fleets, active monitoring of vehicle and driver qualifications can enable a reduction in TCO (total cost of ownership). This can result in a decrease in losses due to theft, theft and negligence, while providing active training programs for drivers.
The way to the future in fleet management
AI-based analytics leveraging IoT, edge computing, and cloud are rapidly changing the way fleet management is done, making it more efficient and effective than ever. AI's ability to evaluate large amounts of data from telematics devices provides managers with valuable information that can be used to improve efficiency of the fleet, reduce costs and increase productivity. From real-time analytics to driver safety management, AI is already changing the way fleets are managed.
Considering that the more data sets AI accumulates through OEM cloud processing, the better the predictions AI can make. This means that safer and more intuitive automated vehicles can be expected in the future, with more accurate routing and real-time vehicle diagnostics.
READ MORE ARTICLES ABOUT: Automotive with AI.
READ THE PREVIOUS POST: Nigerian AI specialist presents new health-focused chatbot.