Architecture and parameters of the FastKAN model.

Driver drowsiness is a leading cause of traffic accidents and fatalities, highlighting the urgent need for intelligent systems capable of real-time fatigue detection. Although recent advancements in machine learning (ML) and deep learning (DL) have significantly improved detection accuracy, most existing models are computationally demanding and not well-suited for deployment in resource-limited environments such as microcontrollers. While the emerging domain of TinyML presents promising avenues for such applications, there remains a substantial gap in the development of lightweight, interpretable, and high-performance models specifically tailored for embedded automotive systems. This paper introduces FastKAN-DDD, an innovative driver drowsiness detection model grounded in the Fast Kolmogorov-Arnold Network (FastKAN) architecture. The model incorporates learnable nonlinear activation functions based on radial basis functions (RBFs), facilitating efficient function approximation with a minimal number of parameters. To enhance suitability for TinyML deployment, the model is further optimized through post-training quantization techniques, including dynamic range, float-16, and weight-only quantization. Comprehensive experiments were conducted using the UTA-RLDD dataset—a real-world benchmark for driver drowsiness detection—evaluating the model across various input resolutions and quantization schemes. The FastKAN-DDD model achieved a test accuracy of 99.94%, with inference latency as low as 0.04 ms and a total memory footprint of merely 35 KB, rendering it exceptionally well-suited for real-time inference on microcontroller-based systems. Comparative evaluations further confirm that FastKAN surpasses several state-of-the-art TinyML models in terms of accuracy, computational efficiency, and model compactness. Our code’s are publicly available at: https://github.com/sihamess/driver_drowsiness_detection_TinyML.驾驶疲劳是引发交通事故与人员伤亡的首要诱因，凸显了研发具备实时疲劳检测能力的智能系统的迫切需求。尽管近年来机器学习（Machine Learning，ML）与深度学习（Deep Learning，DL）的技术进展已大幅提升了检测精度，但现有多数模型计算量庞大，难以在微控制器等资源受限环境中部署。新兴的微型机器学习（TinyML）领域虽为这类应用提供了极具前景的方向，但针对嵌入式车载系统定制开发的轻量、可解释且高性能模型仍存在显著缺口。本文提出了FastKAN-DDD这一创新型驾驶疲劳检测模型，其基于快速柯尔莫哥洛夫-阿诺德网络（FastKAN）架构构建。该模型融入了基于径向基函数（Radial Basis Function，RBF）的可学习非线性激活函数，可通过极少量参数实现高效的函数逼近。为提升模型在微型机器学习部署场景下的适配性，本文还通过动态范围量化、Float16量化以及仅权重量化等后训练量化技术对模型进行了进一步优化。研究团队使用UTA-RLDD数据集——一款面向驾驶疲劳检测任务的真实世界基准数据集——开展了全面实验，针对不同输入分辨率与量化方案对模型进行了评估。FastKAN-DDD模型的测试精度达到99.94%，推理延迟低至0.04毫秒，总内存占用仅为35 KB，极其适配基于微控制器的系统实现实时推理。对比评估进一步证实，FastKAN在精度、计算效率与模型紧凑性上均优于多款当前顶尖的微型机器学习模型。本研究的代码已公开于：https://github.com/sihamess/driver_drowsiness_detection_TinyML。