Tire Track Identification: A Method for Drivable Region Detection in Conditions of Snow-Occluded Lane Lines
- 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.2022-01-0083
- 掲載ページ
- 1-8(Total 8 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) Nicholas A. Goberville, 2) Parth Kadav, 3) Zachary D. Asher |
|---|---|
| 勤務先（英） | 1) Western Michigan University, 2) Western Michigan University, 3) Western Michigan University |
| 抄録（英） | Today’s Advanced Driver Assistance Systems (ADAS) predominantly utilize cameras to increase driver and passenger safety. Computer vision, as the enabler of this technology, extracts two key environmental features: the drivable region and surrounding objects (e.g., vehicles, pedestrians, bicycles). Lane lines are the most common characteristic extracted for drivable region detection, which is the core perception task enabling ADAS features such as lane departure warnings, lane-keeping assistance, and lane-centering. However, when subject to adverse weather conditions (e.g., occluded lane lines) the lane line detection algorithms are no longer operational. This prevents the ADAS feature from providing the benefit of increased safety to the driver. The performance of one of the leading computer vision system providers was tested in conditions of variable snow coverage and lane line occlusion during the 2020-2021 winter in Kalamazoo, Michigan. The results show that this computer vision system was only able to provide high confidence detections in less than 1% of all frames recorded. This is an alarming result, as 21% of all crashes in the U.S. are weather-related. To increase the capabilities of ADAS when snow-occlusions are present, a tire track identification system was developed by comparing various supervised machine learning models. A custom dataset was collected using the Energy Efficient and Autonomous Vehicles lab’s research platform from Western Michigan University. A data preparation pipeline was implemented to label tire tracks and train the machine learning models. The best model achieved high confidence detections of tire tracks in 83% of all frames of which tire tracks were present, an 82% increase in detections than the leading computer vision system provider. 翻訳 |
