A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

• Main Authors: Haiqing Liu, Kunmin Teng, Laxmisha Rai, Yu Zhang, Shengli Wang
• Format: Article
• Language: English
• Published: Wiley 2021-05-01
• Series: IET Intelligent Transport Systems
• Online Access: https://doi.org/10.1049/itr2.12052

Abstract In a new application scenario where the millimetre‐wave radar is installed above the road for detecting traffic flow in downward looking direction, the original data of the radar includes all kinds of background noises and false targets. In order to acquire effective vehicle trajectories, a two‐step abnormal data processing method for millimetre‐wave radar in traffic flow detection application is proposed. In the first step, the rational range of distance, angle and speed are studied, and proper thresholds are presented for reducing the samples of which the single parameter is with the obvious abnormality. Moreover, the nearest neighbour analysis method is used to further extract vehicle trajectories based on the similarity and slope characteristics of each sample to its neighbours. Taking actual detected data as samples, the weighting coefficients, similarity threshold, average slope threshold and standard deviation threshold are calibrated for the proposed nearest neighbour analysis method. The two‐step processing method presents a higher performance in extracting effective trajectory samples, and the ratio of noise points is reduced to 4.1%, compared with 239.9% in the original data sample. The proposed method can provide an effective reference for further applications, such as driving behaviour analysis and traffic flow parameter identification.