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How to empower the edge vehicle with the potential of in-depth data processing and machine learning has generated a lot of discussion in the automotive sector. Manufacturers might reduce data transfer expenses while also enabling the autonomous driving assistance system (ADAS) to react and adjust in real-time. The truth is that moving entirely to the edge is not practical from a financial and pragmatic sense. At least not yet. However, it is conceivable to support some functions at the edge in addition to cloud computing. Let's examine how edge and cloud computing are enhancing one another to produce a more secure and effective transportation environment.
OEMs are assisted by cloud and edge computing to track the condition and operation of their vehicles, including the detection of flaws before they result in potentially fatal recalls.
Edge computing accomplishes this within the car, for instance, with an algorithm that tracks the temperature of the electric vehicle battery and alerts the infotainment system when it rises to dangerous levels. Cloud computing, on the other hand, makes use of data that is transmitted from the car to the cloud. Examining the long-term health of an EV battery and how it relates to driving patterns or environmental factors is one example of this.
The information gathered from the vehicle's sensor is the code for both cloud and edge computing. There is more information to redefine how the system functions and on which to base decisions the more sensors and electronic controllers there are to draw from.
Automakers may examine actual use cases and roll out new services and modifications to increase the functionality and efficiency of their vehicles, thanks to real-time data. The cloud is for deployment and analysis, whereas the edge is for refinement, according to one perspective on the interaction between the two technologies.
For both edge and cloud computing, the telematics control unit (TCU) serves as the entry point to comprehensive vehicle data.
Automotive TCUs have seen a major architectural and functional change when compared to a few years ago. TCUs were created in 2015 with the ability to work with both 1G and 2G networks. On the other hand, contemporary TCUs are built with the capacity to support 3G and 4G, but to connect with 5G.
The key to the future development of the automobile industry is the capacity to combine cloud and edge computing. OEMs will need a system in place to access the extensive, real-world data required to support precise analysis and machine learning algorithms as vehicle complexity keeps increasing.
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