Real-Time AI Inference at the Edge for Self-Driving Cars

Author(s)	Murali Krishna Reddy Mandalapu
Country	United States
Abstract	This article explores the evolution of real-time AI inference systems for autonomous vehicles, focusing on the computational challenges and innovations that enable edge processing of sensor data. It examines the significant data volume generated by modern autonomous vehicles and details the specialized hardware architectures developed to handle these processing demands. The article explores the tradeoffs between edge and cloud computing paradigms, highlighting how each approach addresses different aspects of the autonomous driving challenge. Various model optimization techniques are discussed, including quantization, pruning, knowledge distillation, and hardware-aware neural architecture search, all of which help deploy sophisticated AI models within constrained automotive environments. The article concludes by examining emerging trends that promise to further transform autonomous vehicle computing, including neuromorphic processing, distributed AI architectures, and continuous learning systems, which collectively point toward more adaptive and efficient computational paradigms.
Keywords	Autonomous vehicles, Edge computing, Hardware acceleration, Model optimization, Neuromorphic processing
Field	Computer
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-16
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2462
Short DOI	https://doi.org/g88sbd

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International Journal on Science and Technology