Laser scanners are widely used as the primary sensor for autonomous driving. When the commercialization of autonomous driving is considered, a 2.5-D multi-layer laser scanner is one of the best sensor options. In this paper, a new method is presented to detect pedestrians and vehicles using a 2.5-D multi-layer laser scanner. The proposed method consists of three steps: 1) segmentation; 2) feature extraction; and 3) classification; this paper focuses on the last two steps. In feature extraction, new features for the multi-layer laser scanner are proposed to improve the classification performance. In classification, radial basis function additive kernel support vector machine is employed to reduce the computation time while maintaining the performance. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in many real-life situations.
Bibliographical noteFunding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea within the Ministry of Education, Science and Technology under Grant NRF-2013R1A2A2A01015624. The associate editor coordinating the review of this paper and approving it for publication was Dr. David Hecht. (Corresponding author: Euntai Kim.
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering