International Journal on Science and Technology

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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
Cite This Real-Time AI Inference at the Edge for Self-Driving Cars - Murali Krishna Reddy Mandalapu - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2462
DOI https://doi.org/10.71097/IJSAT.v16.i1.2462
Short DOI https://doi.org/g88sbd
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