论文标题
使用智能手机传感器和复发量化分析的脆弱道路用户检测
Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis
论文作者
论文摘要
随着自动驾驶汽车（AV）行业的快速发展，使用智能手机对脆弱道路使用者（VRU）的检测对于合作智能运输系统（C-ISS）的安全应用至关重要。这项研究探讨了此任务的低功率智能手机传感器以及复发量化分析（RQA）功能。这些特征是在从九个通道中提取的阈值相似性矩阵中计算出来的：加速度计，陀螺仪和每个方向的旋转向量（x，y和z）。考虑到GPS的大功率消耗，GPS数据被排除在外。 RQA功能被添加到传统的时域功能中，以调查使用二进制，四级和五级随机森林分类器时的分类精度。实验结果表明，仅使用RQA功能，其精度为98。34％和98。79％时，表现出令人鼓舞的性能。结果表现优于先前报道的准确性，表明RQA特征相对于VRU检测具有很高的分类能力。
With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). This study explores the use of low-power smartphone sensors and the Recurrence Quantification Analysis (RQA) features for this task. These features are computed over a thresholded similarity matrix extracted from nine channels: accelerometer, gyroscope, and rotation vector in each direction (x, y, and z). Given the high-power consumption of GPS, GPS data is excluded. RQA features are added to traditional time domain features to investigate the classification accuracy when using binary, four-class, and five-class Random Forest classifiers. Experimental results show a promising performance when only using RQA features with a resulted accuracy of 98. 34% and a 98. 79% by adding time domain features. Results outperform previous reported accuracy, demonstrating that RQA features have high classifying capability with respect to VRU detection.
