Ali Elhashemi, Khan Md Nasim, Ahmed Mohamed M
Department of Public Works, Civil Engineering, Faculty of Engineering, Cairo University, Giza, Egypt.
University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States.
J Safety Res. 2022 Dec;83:163-180. doi: 10.1016/j.jsr.2022.08.013. Epub 2022 Sep 1.
This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips.
In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data.
Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data.
Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways.
本研究引入了一种新的分析协议，通过利用从第二次战略公路研究计划（SHRP2）自然驾驶研究（NDS）数据集中提取的轨迹级数据，来检测高速公路上的实时降雪天气状况。数据包括从实时图像特征提取技术中简化的参数、从外部传感器收集的时间序列数据以及NDS自主车辆收集的CAN总线数据。为了在冬季维护中提供灵活性，使用了一分钟和一英里路段这两种分割类型来对降雪行程及其匹配的晴朗天气行程进行采样。
在本研究中，使用六个数据组合开发了四个非参数模型，以检测高速公路上的降雪天气。这些数据组合基于三个数据源进行排列，包括从车载摄像机提取的图像数据库、传感器和CAN总线数据，以检验考虑到数据实时可用性的不同数据类型的降雪检测模型的有效性。
总体而言，所开发的模型成功检测到高速公路上的降雪天气，准确率在76%至89%之间。结果表明，在不访问CAN总线数据的情况下，利用外部传感器数据和图像纹理参数之间的数据融合，可以实现对降雪天气的高精度估计。
可以根据时间或距离坐标、使用不同的数据融合组合以及数据可用性来推动实际应用。该研究证明了在车联网（CV）应用和可变限速（VSL）中使用车辆作为天气传感器以提高高速公路交通安全的重要性。
