Infrastructure-Based LiDAR Monitoring for Assessing Automated Driving Safety
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- 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.2022-01-0081
- 掲載ページ
- 1-9(Total 9 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) Anshuman Srinivasan, 2) Yoga Mahartayasa, 3) Varun Chandra Jammula, 4) Duo Lu, 5) Steven Como, 6) Jeffrey Wishart, 7) Yezhou Yang, 8) Hongbin Yu |
|---|---|
| 勤務先（英） | 1) Arizona State University, 2) Arizona State University, 3) Arizona State University, 4) Rider University, 5) Arizona State University, 6) Science Foundation of AZ, 7) Arizona State University, 8) Arizona State University |
| 抄録（英） | The successful deployment of automated vehicles (AVs) has recently coincided with the use of off-board sensors for assessments of operational safety. Many intersections and roadways have monocular cameras used primarily for traffic monitoring; however, monocular cameras may not be sufficient to allow for useful AV operational safety assessments to be made in all operational design domains (ODDs) such as low ambient light and inclement weather conditions. Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM). Building on earlier work from the IAM in creating an infrastructure- based sensor system to evaluate OSA metrics in real- world scenarios, this paper presents an approach for real-time localization and velocity estimation for AVs using a network of LiDAR sensors. The LiDAR data are captured by a network of three Luminar LiDAR sensors at an intersection in Anthem, AZ, while camera data are collected from the same intersection. Using the collected LiDAR data, the proposed method uses a distance-based clustering algorithm to detect 3D bounding boxes for each vehicle passing through the intersection. Subsequently, the positions and velocities of each detected bounding box are tracked over time using a combination of two filters. The accuracy of both the localization and velocity estimation using LiDAR is assessed by comparing the LiDAR estimated state vectors against the differential GPS position and velocity measurements from a test vehicle passing through the intersection, as well as against a camera-based algorithm applied on drone video footage It is shown that the proposed method, taking advantage of simultaneous data capture from multiple LiDAR sensors, offers great potential for fast, accurate operational safety assessment of AV’s with an average localization error of only 10 cm observed between LiDAR and real-time differential GPS position data, when tracking a vehicle over 170 meters of roadway. 翻訳 |
