Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.

中文翻译：

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基于多模态大数据分析和Sparrow搜索优化的车辆智能分类

车辆智能分类在智能交通系统中起着至关重要的作用。然而，由于动态的交通环境，很难保证分类的准确性。因此，本文采用新型脉冲相干雷达（PCR）采集道路车辆数据，提出了一种基于多模态大数据分析的麻雀搜索算法极限学习机（SSA-ELM）车辆分类方法。首先，通过PCR采集道路车辆数据，提取车辆长度、底盘轮廓和高度特征作为样本数据。然后，利用 ELM 来学习这三个模态特征。根据输入的特征数据，对车辆类型进行分类，包括轿车、越野车和公交车。最后，应用 SSA 优化 ELM 的初始权重和阈值。