基于多尺度特征的路面不平度识别方法

Pavement roughness identification method based on multi-scale features

在智能驾驶技术中,路面不平度识别的结果直接影响着后续驾驶的决策过程。然而,现有的路面不平度识别算法存在准确率低、识别速度慢的问题。针对这一现状,提出了一种基于改进多尺度特征提取网络的隐马尔科夫路面不平度识别方法。该方法在识别准确率和识别速度上均取得了显著提升。首先,改进的多尺度卷积神经网络被用于从原始数据中自动学习并提取多层次的特征。然后,利用t-SNE技术对提取的特征进行可视化处理,以便更好地理解和分析特征分布。最后,利用隐马尔科夫模型对提取的特征进行识别。实验结果表明,对于仿真数据和实际采集数据识别准确率分别达到了99.6%和98.6%,适用于路面不平度识别。

In the field of autonomous driving technology,the identification of pavement roughness directly influences subsequent driving decision-making processes.However,existing algorithms for pavement roughness recognition suffer from issues of low accuracy and slow recognition speed.Addressing this challenge,a Hidden Markov Model based pavement roughness recognition method is proposed,leveraging an improved multi-scale feature extraction network.Significant enhancements in both recognition accuracy and speed are achieved by an enhanced multi-scale convolutional neural network,which autonomously learns and extracts hierarchical features from raw data.Subsequently,t-SNE visualization is applied to the extracted features for improved understanding and analysis of feature distributions.Finally,a Hidden Markov Model is utilized for feature recognition.Experimental results demonstrate recognition accuracies of 99.6%for simulated data and 98.6%for real-world collected data,thereby proving effective for pavement roughness recognition.