Enhanced Road Boundary and Obstacle Detection Using a Downward-Looking LIDAR Sensor

Abstract

Detection of road boundaries and obstacles is essential for autonomous vehicle navigation. In this paper, we propose a road boundary and obstacle detection method using a downward-looking light detection and ranging sensor. This method extracts line segments from the raw data of the sensor in polar coordinates. After that, the line segments are classified into road and obstacle segments. To enhance the classification performance, the estimated roll and pitch angles of the sensor relative to the scanning road surface in the previous time step are then used. The classified road line segments are applied to track the road boundaries, roll, and pitch angles by using an integrated probabilistic data association filter. The proposed method was evaluated with the autonomous vehicle A1, which was the winner of the 2010 Autonomous Vehicle Competition in Korea organized by the Hyundai-Kia automotive group. The proposed method using the estimated roll and pitch angles can detect road boundaries and roadside, as well as road obstacles under various road conditions, including paved and unpaved roads and intersections.