Center for Connected and Automated Driving Research, Korea Transport Institute, 370 Sicheong-daero, Sejong 30147, Korea.
Department of Civil Engineering, McGill University, 817 Sherbrooke Street West, Montreal, QC H3A 0C3, Canada.
Sensors (Basel). 2022 May 10;22(10):3611. doi: 10.3390/s22103611.
The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure the safety of urban transportation systems using various sensors and communication devices. However, realizing a data flow framework, including data collection, data transmission, and data processing, in South Korea is challenging, as CAVs produce a massive amount of data every minute, which cannot be transmitted via existing communication networks. Thus, raw data must be sampled and transmitted to the server for further processing. The data acquired must be highly accurate to ensure the safety of the different agents in C-ITS. On the other hand, raw data must be reduced through sampling to ensure transmission using existing communication systems. Thus, in this study, C-ITS architecture and data flow are designed, including messages and protocols for the safety monitoring system of CAVs, and the optimal sampling interval determined for data transmission while considering the trade-off between communication efficiency and accuracy of the safety performance indicators. Three safety performance indicators were introduced: severe deceleration, lateral position variance, and inverse time to collision. A field test was conducted to collect data from various sensors installed in the CAV, determining the optimal sampling interval. In addition, the Kolmogorov-Smirnov test was conducted to ensure statistical consistency between the sampled and raw datasets. The effects of the sampling interval on message delay, data accuracy, and communication efficiency in terms of the data compression ratio were analyzed. Consequently, a sampling interval of 0.2 s is recommended for optimizing the system's overall efficiency.
城市交通系统的安全性被认为是一个全球性的公共卫生问题，许多研究人员为此做出了贡献，以提高其安全性。联网自动驾驶汽车（CAV）和协同智能交通系统（C-ITS）被认为是确保城市交通系统安全的解决方案，它们使用各种传感器和通信设备。然而，在韩国实现包括数据收集、数据传输和数据处理的数据流程框架具有挑战性，因为 CAV 每分钟会产生大量数据，而这些数据无法通过现有的通信网络传输。因此，必须对原始数据进行采样并传输到服务器进行进一步处理。为了确保 C-ITS 中不同代理的安全性，获取的数据必须高度准确。另一方面，必须通过采样减少原始数据，以确保使用现有通信系统进行传输。因此，在本研究中，设计了 C-ITS 架构和数据流，包括 CAV 安全监测系统的消息和协议，以及在考虑通信效率和安全性能指标准确性之间的权衡下，确定数据传输的最佳采样间隔。引入了三个安全性能指标：严重减速、横向位置方差和碰撞时间倒数。进行了现场测试，从安装在 CAV 中的各种传感器收集数据，确定最佳采样间隔。此外，还进行了柯尔莫哥洛夫-斯米尔诺夫检验，以确保采样数据集和原始数据集之间的统计一致性。分析了采样间隔对消息延迟、数据准确性以及数据压缩比方面的通信效率的影响。因此，建议采用 0.2s 的采样间隔来优化系统的整体效率。
