New innovations in technology have paved a way for vehicles to be smarter and more intelligent in their Underground Fleet Management System . Traditionally engineered to be more mechanical by design, many modern vehicles today have transformed into digital computers on wheels with the introduction of embedded electronics and microprocessors. These embedded computing processors provide a central nervous system for the processing and monitoring of specific sensor inputs throughout a modern vehicle. For example, automotive OEMs include hundreds of these embedded microprocessors in order to manage a wide-network of sensor inputs/outputs for specific functions and the safety of the vehicle. The introduction of these embedded microprocessors in vehicles have ultimately transformed the entire history of vehicles in an incredible and intelligent way. This has been proven with all the technological features behind modern day vehicles.
Modern day vehicles leverage these electronic controllers and can tap into valuable data insights that provide major benefits in terms of automation for vehicles. Many of those features that come almost second nature to consumers like power steering, traction control, air condition, transmission, and air bags are all controlled by an embedded microcontrollers and sensors. Especially as advancements in machine learning and new AI technologies help shape the future for autonomous driving vehicles, new edge computing platforms will be pivotal in the next wave of real-time processing, data analytics, and especially vehicle telematics. For example Tesla’s current line of production vehicles provide semi-autonomous driving by leveraging a network of sensors for its advanced driver-assistance systems (ADAS). At its core, most connected and intelligent ADAS features are made possible from edge computing solutions that provide robust, real-time processing capabilities directly from senor data. Ruggedized edge computers will bring even more processing power and real-time intelligence for many automotive applications, eventually paving a new era of autonomous commercial vehicles. . Vehicle fleet telematics and the data that is generated is accelerating benefits of data collection, especially in edge computing applications for the automotive markets. Many of these applications are collecting a significant amount of data that will assist with building machine learning algorithms for an autonomous future.