| 摘要: |
| 在智能驾驶的环境感知领域，毫米波雷达是一种关键的传感器技术。然而，因数据量有限，其特征数据的采集具有一定的挑战性，这限制了环境感知分类模型的训练效果。针对这一难题，提出了一种融合自注意力机制的卷积长短期记忆网络模型，旨在预测并生成毫米波雷达点云的特征数据，以此来扩展雷达特征数据集。首先采集道路目标的运动状态数据，对数据进行二维快速傅里叶变换、恒虚警率检测，并利用多输入多输出（Multiple-Input Multiple-Output，MIMO）技术提升方位分辨率；接着执行点云聚类及特征提取；最后采用含注意力机制的卷积长短期记忆网络对特征数据进行进一步处理与预测。在真实采集的3类道路目标数据集上，与其他模型相比，该方法在不同道路目标运动特征的预测玆2上提高了1%~7%。 |
| 关键词: 毫米波雷达 道路环境感知 点云特征数据 注意力机制 时序预测 |
| DOI：10.20079/j.issn.1001-893x.240118005 |
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| 基金项目: |
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| Enhancement of mmWave Radar Point Cloud Feature Data Based on Self-attention Mechanism CNN-LSTM |
| ZHANG Chunjie,CHEN Qi,ZHAO Jiaqi |
| (Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China) |
| Abstract: |
| In the field of environment perception for intelligent driving,millimeter-wave radar serves as a crucial sensor technology.However,the feature data acquisition from radar is a challenge due to limited data size,thus hindering the training effectiveness of perception classification models.To address this issue,the authors propose a convolutional long short-term memory(LSTM) network model integrated with a self-attention mechanism,aiming to predict and generate feature data for millimeter-wave radar point clouds,thereby expanding the radar feature dataset.First,motion state data of road targets is collected and two-dimensional fast Fourier transform and constant false alarm rate detection are performed.Multiple-input multiple-output(MIMO) technology is used to enhance azimuth resolution.Second,clustering point clouds and extracting features are performed.Finally,the convolutional LSTM network with an attention mechanism is adopted to further process and predict feature data.In comparison with other models on three types of real-world road target datasets,this method has achieved a 1% to 7% improvement in 玆2 prediction of various road target motion characteristics. |
| Key words: millimeter-wave radar road object detection point cloud feature data attention mechanism time series forecasting |
