Cyclist detection and tracking based on multi-layer laser scanner

Mingfang Zhang,Li Wang,Rui Fu,Yingshi Guo,Pangwei Wang,Hui Deng

doi:10.1186/s13673-020-00225-x

Abstract

The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

Highlights

Autonomous vehicle has attracted great interest in much ongoing research [1, 2], and environment perception is an essential step toward the trajectory plan for autonomous vehicle [3,4,5]
In this paper, cyclist detection and tracking method is presented based on multi-layer laser scanner
Subarea-based Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is developed to segment the uneven point cloud based on the density distribution

Summary

Introduction

Autonomous vehicle has attracted great interest in much ongoing research [1, 2], and environment perception is an essential step toward the trajectory plan for autonomous vehicle [3,4,5]. The contributions of this paper are twofold: (1) It is the first attempt to separate the raw point cloud into several subareas based on the density distribution; (2) CS model is selected as the motion model of the cyclist, and MHT algorithm is used to track multiple cyclists. 2D laser scanner generates the sparse single-layer point cloud which is inadequate for object classification in real driving environment. Feature extraction 37-dimensional feature set is proposed for cyclist detection and tracking based on the point cloud characteristics of the cyclist. This feature set includes 11 number-ofpoints-based features, 16 geometric features and 10 statistical features, as listed in Tables 1, 2 and 3.

Experiments

Findings

Conclusion