A Novel Edge Computing-Based Real Time Object Detection for Autonomous Vehicles

Object detection in real-time would play a critical role in safe navigation, evasion of obstacles, and decision making of autonomous cars. However, the latency and the bandwidth limitations imposed by the established cloud-based processing paradigm degrade the sensitivity and reliability required to perform autonomous driving. In this study, an advanced technique of real-time object detection that uses edge computing, applicable to driverless vehicles, is offered. This drastically reduces the time spent in inference spatially but maintains promising levels of detection accuracy by offloading compute-intensive work to edge nodes, which are deployed closer to the vehicle, for example, at roadside units or onboard processors. In order to guarantee effective processing under various network and environmental conditions, the suggested system incorporates lightweight deep learning models optimized for edge hardware, using adaptive model compression and intelligent workload distribution. Experimental tests show that, without sacrificing detection accuracy, our method can reduce latency by up to 40% and boost energy economy by up to 25% when contrasted to traditional cloud-dependent methods. The system is a good option for practical implementation since it also demonstrates strong scalability and flexibility in dynamic driving situations. This study highlights how edge computing, which strikes a mix amongst speed, accuracy, and resource efficiency, has the possibility to revolutionize next-generation autonomous car systems.

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