LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings

Author:

Shen Tuo12,Zhou Jinhuang2,Yuan Tengfei3,Xie Yuanxiang2,Zhang Xuanxiong1

Affiliation:

1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

2. Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China

3. SILC Business School, Shanghai University, Shanghai 201800, China

Abstract

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

Funder

National Key Research and Development Project of China

Science and Technology Commission of Shanghai Municipality

Natural Science Foundation of Shanghai

Publisher

MDPI AG

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