基于FMCW毫米波雷达的驾驶员头手动作识别
Driver’s Head-Hand Action Recognition Based on FMCW Millimeter-Wave Radar

随着经济水平上升，汽车逐渐成为人们出行的主要选择之一。车内驾驶员的驾驶状态是影响行驶安全的重要因素，而当前的基于视觉识别的驾驶员动作和状态检测易受光照和遮挡等问题影响，且涉及用户隐私问题。毫米波雷达具有高探测精度，高集成、不受光线等因素影响、低成本等优点，已经广泛应用与体征信号、动作识别等领域，但目前对于驾驶姿态的动作识别种类较少。为此，本文基于77 GHz毫米波雷达，对驾驶员在车内动作进行信号采集，构建了包含静止、点头、左右环视、顿头(瞌睡)、前后剧烈晃动(急刹)、手部平移(抽烟)、手部抬起(打电话)七种动作的数据集。同时开发了基于VGG16-LSTM-CBAM的深度学习网络模型，对微多普勒频谱图进行分类识别。实验结果显示，本文提出的模型识别准确率达到99.16%，有效地提高了对驾驶员头手协同动作的识别精度。
As the economic level rises, automobile gradually becomes one of the main choices for people’s traveling. The driving status of the driver in the car is an important factor that affects driving safety, and the current visual recognition-based driver action and status detection is susceptible to problems such as light and occlusion, and involves user privacy issues. Millimeter-wave radar has the advantages of high detection accuracy, high integration, insensitivity to light and other factors, and low cost, and thus has been widely used in the fields of body signals and action recognition. However, for recognition of driver’s postures, existing studies are limited to only a few actions. In this paper, using a 77 GHz millimeter wave radar, we constructed a dataset containing seven kinds of driver’s actions, including stationary, nodding head, left and right looking around, head-stopping (dozing), front and rear violent shaking (sharp braking), hand panning (smoking), hand lifting (phone call). A deep learning network model based on VGG16-LSTM-CBAM is also developed to classify and recognize micro-Doppler spectrograms. The experimental results show that the recognition accuracy of the proposed model reaches 99.16%, which effectively improves the recognition accuracy of driver actions.