论文标题
嵌入式系统性能分析，用于为驾驶员实施便携式嗜睡检测系统
Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers
论文作者
论文摘要
道路上的嗜睡是致命后果的普遍问题。因此，已经提出了许多系统和技术。在现有方法中，Ghoddoosian等人。利用时间闪烁的模式来检测早期嗜睡的迹象，但是它们的算法仅在强大的台式计算机上进行了测试，该台式计算机在移动的车辆设置中不可用。在本文中，我们提出了一个有效的平台来运行Ghoddosian的算法，详细介绍了我们运行的性能测试以确定该平台，并解释了我们的阈值优化逻辑。在考虑了Jetson Nano和Beelink（迷你PC）之后，我们得出结论，Mini PC是在车辆中运行嵌入式系统的最有效和实用的。为了确定这一点，我们进行了通信速度测试并评估了推理操作的总处理时间。基于我们的实验，用于运行嗜睡模型的平均总处理时间为94.27毫秒的Jetson Nano，而Beelink（Mini PC）的平均处理时间为22.73 ms。考虑到每个设备的可移植性和功率效率以及处理时间结果，Beelink（Mini PC）被确定为最合适的。另外，我们提出了一种阈值优化算法，该算法根据嗜睡检测模型的灵敏度和特异性之间的权衡来确定驱动程序昏昏欲睡还是警报。我们的研究将成为嗜睡检测研究及其在车辆中的应用的关键下一步。通过我们的实验，我们确定了一个有利的平台，该平台可以实时运行嗜睡算法，并可以用作进一步提高嗜睡检测研究的基础。这样一来，我们弥合了现有的嵌入式系统与其在车辆中的实际实施之间的差距，以使嗜睡技术更接近现实生活中的实施。
Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.
