Edge Intelligence in Autonomous Vehicle Navigation

Autonomous vehicles are designed and built with advanced technology. This paper highlights the overview of autonomous vehicle navigation, decision-making, mapping, and localization, along with the implementation of edge intelligence in autonomous vehicles. Edge intelligence in AV is responsible for monitoring the surroundings, decision-making on the road, and navigation. The sensors implemented in the vehicles collect the surrounding information and process it using edge intelligence. Algorithms like Convolutional Neural Networks monitor lane detection and object classification, and the YOLO algorithm monitors real-time object identification as well as detection. It is fast and accurate, the V2X technology is used to exchange data between neighbouring cars, which can improve navigation faster at the destination. Autonomous vehicles can make wise decisions and navigate complex and unfamiliar road conditions on the path without human involvement. The edge intelligence reduces the latency. The challenges faced in the AV, such as Connectivity challenges, Sensor limitations, and navigation accuracy, can be inspected and solved using Edge intelligence. Edge intelligence strengthens adaptability and further navigation toward better transport safety in complex environments.