December 2025
Intermediate to advanced
656 pages
16h 47m
English
Content preview from Edge Artificial Intelligence
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15The Role of Edge Computing in Enhancing Autonomous Vehicle Performance
Lakshmeesh Mankame1, Anushka Raspayle1, Onkar Mane1, Preeti Agarwal1* and Anchit Bijalwan2
1School of Technology Management & Engineering, Narsee Monjee Institute of Management Studies (Deemed to-be University), Navi Mumbai, Maharashtra, India
2School of Computing and Innovative Technologies, British University Vietnam, Hanoi, Vietnam
Abstract
Vehicular edge computing is, therefore, one of the strongest key building blocks that would connect to autonomous vehicles and would give priority in focusing latencies for carryout decision-making that tends to be very sensitive when time is in play. This is achieved because VEC brings computation closer to the vehicles, thereby increasing the speed of data transmission since the AI models can analyze the data and inform the vehicles quickly for optimal operation and safety. CAVs systems focus on addressing security and privacy issues which federated learning provides an excellent fix. This would ensure that in this model several statistical models are built as well as trained within these edge devices. All these will not need the accumulation of all data in a singular area, hence no chance of opening the sometimes-sensitive data all at the same time increasing the models. There would be utilization of URLLC in the 6G networks in the future developments in moving towards VEC. That is further minimization of any form of latency. It is to achieve enhanced communications ...
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