| Title |
Driving Environment Perception and Grid Map Generation System Using Deep Learning Based Mono Vision |
| Authors |
조은기(Eungi Cho) ; 김현석(Hyeonseok Kim) ; 박성근(Seongkeun Park) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.2.356 |
| Keywords |
Deep learning; Distance estimation; Occupancy grid map; Driving environment; Autonomous vehicle |
| Abstract |
In this paper, we propose a driving environment recognition and grid map generation system based on deep learning network using only data acquired with Mono vision. YOLO(You Only Look Once)v3 and FCN are used to recognize the driving environment, and object detection and driving area detection are performed respectively. In addition, the occupancy grid map is generated using the respective network results and vehicle movement information. The data used in this study is based on the KITTI dataset. According to the results, the proposed method uses mono vision, but can obtain distance information and has higher performance than the result of generating a grid map using a single sensor. |