Authors:
Sung-ju Kim
and
Soon-Yong Park
Affiliation:
Kyungpook National University, Korea, Republic of
Keyword(s):
Lane-level Vehicle Positioning, Ego-lane Detection, ADAS, Autonomous Driving, Driver Assistant, SVM, Stereo Matching, Traffic Sign Detection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Stereo Vision and Structure from Motion
;
Tracking and Visual Navigation
Abstract:
Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) or DGPS (Differential GPS) techniques are generally used in lane-level poisoning systems, which only provide an accuracy level up to 2-3 m. In this paper, we introduce a vision based lane-level positioning technique that provides more accurate prediction results. The proposed method predicts the current driving lane of the vehicle by tracking the 3D location of the traffic signs that are in the side-way of the road using a stereo camera. Several experiments are conducted to analyse the feasibility of the proposed method in driving lane level prediction. According to the experimental results, the proposed method could achieve 90.9% accuracy.