An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar.

Lin Cao,Kangning Du,Xinyi Zhang,Chong Fu,Tao Wang

doi:10.3390/s20174920

Abstract

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

Highlights

In the intelligent transportation system, multi-target traffic radar is used as a road traffic assistance tool to obtain information such as the speed and distance of vehicles in multiple lanes of the radar irradiation area, which is widely used in road speeding bayonet snapshots and traffic Information monitoring [1,2]
The experiment selected four sets of actual scene data obtained by multi-target traffic radar to explain the performance of the adaptive ellipse distance density peak fuzzy (AEDDPF) algorithm
To show that the initial clustering center point obtained by the AEDDPF algorithm is closer to the real data center, this paper introduces distance error rate (DER) as the evaluation index of the initial clustering center, the expression is DER = √

Summary

Introduction

In the intelligent transportation system, multi-target traffic radar is used as a road traffic assistance tool to obtain information such as the speed and distance of vehicles in multiple lanes of the radar irradiation area, which is widely used in road speeding bayonet snapshots and traffic Information monitoring [1,2]. The multi-target radar determines the congestion of vehicles on the current road section based on the information detected at important road sections such as traffic light intersections. Multi-target radar has become one of the most used road equipment in intelligent transportation systems and is widely applied to vehicle flow statistics, speeding vehicle monitoring, and lane congestion information judgment [10]

Results

Discussion

Conclusion