Abstract:  A fusion scheme with 4 lines LADAR (laser detection and ranging) sensor and camera was adopted to provide more precise detection for traffic, for an intelligent vehicle. Firstly, by using deep learning technique to detect image. Then, mapping LADAR data to image through a space transfer matrix. Finally, by using an R-Tree algorithm to quickly match LADAR points and corresponding detection boxes. The traffic’s real location was calculated easily by laser’s ranging. The proposed fusion frame was tested by images and point cloud data collected from real motorway scenes. The results show that the false negative (FN) of the fusion frame method is 8.03%, which is lower than that of 14.86% come from the Mask R-CNN method. Therefore, the fusion data could decrease probability of the FN compare with single data.

Key words: intelligent vehicles, object detection, laser detection and ranging (LADAR), point cloud data, image detection, convolutional neural network, multi-sensors fusion, R-Tree algorithm