Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars. Issue 2 (14th September 2022)

• Record Type: Journal Article
• Authors: Lei, Cailin; Zhao, Cong; Ji, Yuxiong; Shen, Yu; Du, Yuchuan
• Is Part Of: IET intelligent transport systems. Volume 17:Issue 2(2023)
• Page Start: 418
• Page End: 434
• Publication Date: 2022-09-14
• Subjects: Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12268
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars.