Vehicle and Pedestrian Recognition Using Multilayer Lidar based on Support Vector Machine

Abstract

Moving-object tracking (estimating position and velocity of moving objects) is a key technology for autonomous driving systems and driving assistance systems in mobile robotics and vehicle automation domains. To predict and avoid collisions, the tracking system has to recognize objects as accurately as possible. This paper presents a method for recognizing vehicles (cars and bicyclists) and pedestrians using multilayer lidar (3D lidar). Lidar data are clustered, and eight-dimensional features are extracted from each of clustered lidar data, such as distance from the lidar, velocity, object size, number of lidar-measurement points, and distribution of reflection intensities. A multiclass support vector machine is applied to classify cars, bicyclists, and pedestrians from these features. Experiments using “The Stanford Track Collection” data set allow us to compare the proposed method with a method based on the random forest algorithm and a conventional 26-dimensional feature-based method. The comparison shows that the proposed method improves recognition accuracy and processing time over the other methods. Therefore, the proposed method can work well under low computational environments.