In this study, the authors propose an effective real‐time approach for intersection detection and recognition during autonomous driving in an unknown urban environment. The authors approach use point cloud data acquired by a three‐dimensional laser scanner mounted on the vehicle. Intersection detection and recognition are formulated as a classification problem whereby roads are classified as segments or intersections and intersections are subclassified as T‐shaped or +‐shaped. They first construct a novel model called a virtual cylindrical scanner for efficient feature‐level representation of the point cloud data. Then they use support vector machine classifiers to resolve the classification problem according to the features extracted. A series of experiments on real‐world data sets and in a simulation environment demonstrate the effectiveness and robustness of the authors approach, even in highly dynamic urban environment. They also performed simulation experiments to investigate effects of several critical factors on their proposed approach, such as other vehicles on the road and the advance detection distance.
Intersection detection and recognition for autonomous urban driving using a virtual cylindrical scanner
IET Intelligent Transport Systems ; 8 , 3 ; 244-254
2014-05-01
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
support vector machine classifier , unknown urban environment , real‐time approach , optical scanners , autonomous urban driving , feature‐level representation , simulation environment , T‐shaped intersections , intersection detection , virtual cylindrical scanner , three‐dimensional laser scanner , automated highways , shape recognition , intersection recognition , feature extraction , road classification , support vector machines , +‐shaped intersections , point cloud data acquisition , real‐world data sets , dynamic urban environment , image classification , data acquisition
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