Abstract
There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.
Highlights
When an autonomous vehicle drives on urban roads, the vehicle encounters a number of traffic intersections and is expected to pass the intersections smoothly all together with other vehicles without causing any trouble
NNclassifier classifier based on proposed the proposed similarity is to applied to the intersection type based on the similarity is applied the intersection type recognition
Unlike the previous previous works, the occupancy grid map was built relative to the local coordinate and the works, the occupancy grid map was built relative to the local coordinate and the intersection type intersection type was recognized based on the local OGM
Summary
When an autonomous vehicle drives on urban roads, the vehicle encounters a number of traffic intersections and is expected to pass the intersections smoothly all together with other vehicles without causing any trouble. The 3D multi-layer laser scanner has some problems: it is economically too expensive to commercialize and the associated algorithm is computationally too expensive to implement in real-time This sensor ruins the design of the vehicles. The previous intersection type recognition methods using 2D multi-layer laser scanners have some drawbacks: First, the associated algorithms are computationally expensive because they require building a wide global occupancy grid map. A new method for the intersection type recognition using a multi-layer laser scanner is proposed. Compared with the previous works [13,14,15], the proposed method builds the occupancy grid map (OGM) not relative to the global coordinate but relative to the local coordinate, relative to the ego vehicle coordinates.
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