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一个高性能和强大的FPGA实现一个驱动器状态监控应用程序

P Christakos¹, N Petrellis¹, P Mousouliotis²

¹Electrical and Computer Engineering, University of Peloponnese, 263 34 Patras, Greece.

Sensors (Basel, Switzerland)

|July 29, 2023

概括

本研究介绍了一种高性能驾驶员状态监测 (DSM) 系统,使用回归树集 (ERT) 检测驾驶员的嗜睡. 优化的硬件加速实现了高率,用于准确,强大的昏昏欲睡检测.

科学领域:

计算机视觉和机器学习
汽车安全系统汽车安全系统
可重新配置的硬件加速器

背景情况:

司机昏昏欲睡是道路交通事故的重要原因之一.
现有的驱动器状态监控 (DSM) 系统经常面临实时处理和准确性的挑战.
机器学习方法,如回归树集 (ERT),显示出面部地标分析的希望.

研究的目的:

开发和实施一款高性能驾驶员状态监控 (DSM) 应用程序,用于检测驾驶员的昏昏欲睡.
为了加速面部地标对齐过程,使用可重新配置的硬件.
通过结合面部形状连贯性规则来提高嗜睡检测的稳定性和准确性.

主要方法:

利用回归树集 (ERT) 机器学习算法对准68个面部地标.
移植和调整了一个开源的ERT实现,用于可重新配置的硬件,以加快处理速度.
采用非传统的硬件加速技术来管理大数据传输和计算内核中的低数据重用.

主要成果:

实现高处理速率,高达每秒65.
通过忽略错误检测和估计,证明了高稳定性和准确性.
报告说,哈欠识别的灵敏度和精度分别达到93%和97%.

结论:

在可重新配置的硬件上实现的DSM算法为驾驶员昏昏欲睡的检测提供了高性能解决方案.
开发的硬件加速技术对于具有苛刻数据传输要求的机器学习应用程序是有效的.
这种方法为通过先进的驾驶员监控来提高汽车安全提供了有希望的方向.

关键词:

维蒂斯 HLS 的司机昏昏欲睡的情况司机昏昏欲睡的情况面部检测的有效性硬件加速加速器硬件加速器机器学习是机器学习.形状对齐,形状的对齐.

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