摘要
针对当前无人车行驶过程中存在的异常信号测试手段缺乏问题,以可靠性行驶试验中受到环境干扰等导致的信号异常为例,利用感知系统中多传感器感知信号在时域和空域上的相关性,建立多传感器采集数据信息间的交叉数学模型,提出将传感器采集信号和各传感器分别作为信号矩阵的行列元素,通过此数值化方式将原始多传感器信号转化为可参数化的信号矩阵模型。还提出了一种基于矩阵补全和深度矩阵分解融合(MC+DMF)的方法恢复由于环境干扰等导致的部分异常信号,根据神经网络前向传播特性,将原始矩阵中的行向量(单个传感器在i时刻的各采集数据)和列向量(传感器向量)进行降维,降低失真信号矩阵在特征提取过程中的计算量,利用Hadamard乘积对特征提取后的损失函数正则化避免出现过拟合,将该方法运用至SODA10M和KITTI公开数据集中,并与传统的单一矩阵分解(MF)、概率矩阵分解(PMF)、带偏置的奇异值分解(Bias-SVD)方法进行均方根误差(MAE)对比试验,可有效检测到行驶过程中出现的异常信号传感器。结果表明,深度矩阵分解方法能极大地降低数据恢复误差和时间,相较于概率矩阵分解方法,其误差率低1%,恢复时间少约20.65%。
Aiming at the lack of effective methods for testing abnormal signals during the operation of unmanned vehicles,the paper focuses on signal anomalies caused by environmental disturbances in reliability driving tests.By using the correlation of signals from multiple sensors in both the time domain and spatial domain,a cross-mathematical model is established based on the multi-sensor data.The signals collected from sensors are assigned as the row elements and the sensors as the column elements within the signal matrix.This numerical method transforms the original multi-sensor signals into a parameterized signal matrix model.A method combining matrix completion and deep matrix decomposition fusion(MC+DMF)is proposed to recover certain abnormal signals resulting from environmental disturbances.According to the forward propagation characteristics of the neural network,dimensionality reduction is applied to the row vectors(data collected by individual sensors at time i)and column vectors(sensor arrays)in the original matrix.This process reduces the computational load during feature extraction from the distorted signal matrix.Additionally,the Hadamard product is used to regularize the MC+DMF loss function after feature extraction to avoid overfitting.The proposed method is applied on the SODA10M and KITTI public datasets,and comparing with traditional approaches,such as the single matrix factorization(MF),probability matrix factorization(PMF)and Bias-SVD,the experiments using root mean square error(RMSE)show that the method can effectively detect abnormal sensor signals caused by vibration interference during driving.The results show that the MC+DMF method can greatly reduce the data recovery error and time.Compared with the probability matrix decomposition method,it achieves a 1%lower error rate and approximately 20.65%less recovery time.
作者
钟岳
徐峰
张纬华
ZHONG Yue;XU Feng;ZHANG Weihua(Unit 32184 of the PLA,Beijing 100071,China)
出处
《汽车工程学报》
2024年第5期801-811,共11页
Chinese Journal of Automotive Engineering
基金
国家自然科学基金项目(61671470)。
关键词
环境干扰
异常信号
矩阵补全
多传感器融合
深度矩阵分解
environmental disturbances
abnormal signals
matrix completion
multisensor fusion
deep matrix factorization
