A TinyML ‐Powered Pedestrian Detection Framework for IoT ‐Edge Nodes in Autonomous Vehicles

In the Internet of Vehicles (IoV), a critical subset of the Internet of Things (IoT), autonomous vehicles function as sophisticated mobile edge nodes that must process vast streams of sensor data in real‐time. Accurate pedestrian detection is a safety‐critical task for these nodes, yet delivering high recall for tiny and distant pedestrians on milliwatt‐class IoT hardware remains a significant challenge. We introduce a TinyML‐oriented detector that couples (i) a sparsity‐aware, pruned real‐time detection architecture, (ii) an ultra‐light channel–spatial attention block, and (iii) a progressive pruning and mixed‐precision quantization pipeline co‐optimized for resource‐constrained IoT‐edge platforms. The final network is 86% smaller than its uncompressed baseline and runs at 12 FPS within a 5.3 W power budget on a representative embedded IoT device. Across CityPersons, Caltech‐USA, and KITTI datasets, our model consistently outperforms recent lightweight detectors, achieving a 15 percentage‐point reduction in the log‐average miss rate for the challenging case of pedestrians under 50 pixels. The results demonstrate that our hardware‐aware, TinyML approach enables reliable, real‐time pedestrian perception on low‐power automotive IoT‐edge nodes.