Real-time Detection and Avoidance of Obstacles in the Path of Autonomous Vehicles Using Monocular RGB Camera
- 提供方法
- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2022-01-0074
- 掲載ページ
- 1-11(Total 11 p)
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
| 著者（英） | 1) Apurbaa Mallik, 2) Meghana Laxmidhar Gaopande, 3) Gurjeet Singh, 4) Aniruddh Ravindran, 5) Zafar Iqbal, 6) Steven Chao, 7) Hitha Revalla, 8) Vijay Nagasamy |
|---|---|
| 勤務先（英） | 1) Ford Motor Company, 2) Ford Motor Company, 3) Ford Motor Company, 4) Ford Motor Company, 5) Ford Motor Company, 6) Ford Motor Company, 7) Ford Motor Company, 8) Ford Motor Company |
| 抄録（英） | In this paper, we present an end-to-end real-time detection and collision avoidance framework in an autonomous vehicle using a monocular RGB camera. The proposed system is able to run on embedded hardware in the vehicle to perform real-time detection of small objects. RetinaNet architecture with ResNet50 backbone is used to develop the object detection model using RGB images. A quantized version of the object detection inference model is implemented in the vehicle using NVIDIA Jetson AGX Xavier. A geometric method is used to estimate the distance to the detected object which is forwarded to a MicroAutoBox device that implements the control system of the vehicle and is responsible for maneuvering around the detected objects. The pipeline is implemented on a passenger vehicle and demonstrated in challenging conditions using different obstacles on a predefined set of waypoints. Our results show that the system is capable of detecting objects that appear in an image area as small as 20x30 pixels in a 1280x720 image and can run at a speed of 24 frames per second (FPS) on the embedded device in the vehicle. A data analyzer is also employed to visualize the real-time performance of the system. 翻訳 |
