マルチセンサ融合による停止線推定の統合的グリッド融合手法
Holistic Grid Fusion Based Stop Line Estimation
ステレオカメラとLiDARのデータを融合し、CNNで交差点の停止線を検出する手法を提案。遮蔽時や最大50m先でも検出可能で、カメラのみより検出範囲が向上する。
著者: Runsheng Xu, Faezeh Tafazzoli, Li Zhang, Timo Rehfeld, Gunther Krehl, Arunava Seal
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufficient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.
