Deer Crossing Road Detection With Roadside LiDAR Sensor

Loading...
Thumbnail Image

Authors

Chen, Jingrong
Xu, Hao
Wu, Jianqing
Yue, Rui
Yuan, Changwei
Wang, Lu

Issue Date

2019

Type

Article

Language

Keywords

roadside LiDAR , Deer crossing , object classification , vehicle trajectories

Research Projects

Organizational Units

Journal Issue

Alternative Title

Abstract

Deer crossing roads are a major concern of highway safety in rural and suburban areas in the United States. This paper provided an innovative approach to detecting deer crossing at highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, and object classification. An automatic background filtering method based on the point distribution was applied to exclude background but keep the deer (and road users if they exist) in the space. A modified density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for object clustering. Adaptive searching parameters were applied in the vertical and horizontal directions to cluster the points. The cluster groups were further classified into three groups-deer, pedestrians, and vehicles, using three different algorithms: naive Bayes, random forest, and k-nearest neighbor. The testing results showed that the random forest (RF) can provide the highest accuracy for classification among the three algorithms. The results of the field test showed that the developed method can detect the deer with an average distance of 30 m far away from the LiDAR. The time delay is about 0.2 s in this test. The deer crossing information can warn drivers about the risks of deer-vehicle crashes in real time.

Description

Citation

Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., & Wang, L. (2019). Deer Crossing Road Detection With Roadside LiDAR Sensor. IEEE Access, 7, 65944�"65954. doi:10.1109/access.2019.2916718

Publisher

License

Open Access

Journal

Volume

Issue

PubMed ID

ISSN

2169-3536

EISSN

Collections