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Deer Crossing Road Detection With Roadside LiDAR Sensor
Date
2019Type
ArticleAbstract
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.
Permanent link
http://hdl.handle.net/11714/6118Additional Information
Journal Title | IEEE Access |
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Rights | Open Access |
Rights Holder | IEEE; (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |