期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 13, 期 2, 页码 974-982出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2011.2179802
关键词
Autonomous driving; lane detection; obstacle detection; pedestrian-crossing detection; speed-bump detection
资金
- Korea Research Foundation through the SNU-IAMD [KRF-2009-200-D00003]
- BK21 Program
- Korean Government through the Ministry of Education, Science, and Technology
- National Research Foundation of Korea [2010-0001958]
- Korean Government
- National Research Foundation of Korea [220-2009-1-D00003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents an environment-detection-and-mapping algorithm for autonomous driving that is provided in real time and for both rural and off-road environments. Environment-detection-and-mapping algorithms have been designed to consist of two parts: 1) lane, pedestrian-crossing, and speed-bump detection algorithms using cameras and 2) obstacle detection algorithm using LIDARs. The lane detection algorithm returns lane positions using one camera and the vision module VisLab Embedded Lane Detector (VELD), and the pedestrian-crossing and speed-bump detection algorithms return the position of pedestrian crossings and speed bumps. The obstacle detection algorithm organizes data from LIDARs and generates a local obstacle position map. The designed algorithms have been implemented on a passenger car using six LIDARs, three cameras, and real-time devices, including personal computers (PCs). Vehicle tests have been conducted, and test results have shown that the vehicle can reach the desired goal with the proposed algorithm.
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