Lightweight Map Updating for Highly Automated Driving in Non-paved Roads
Highly autonomous vehicles have drawn the interests of many researchers in recent years. For highly autonomous vehicles, a high-definition (HD) map is crucial since it provides accurate information for autonomous driving. However, due to the possible fast-changing environment, the performance of HD maps will deteriorate over time if timely updates are not ensured. Therefore, this paper studies the updating of lightweight HD maps in closed areas. Firstly, a novel two-layer map model called a lightweight HD map is introduced to support autonomous driving in a flexible and efficient way. Secondly, typical updating of scenarios in closed areas with non-paved roads is abstracted into operations including area border expansion, road addition, and road deletion. Meanwhile, a map updating framework is proposed to address the issue of map updating in closed areas. Finally, an experiment is conducted to demonstrate the feasibility and effectiveness of the proposed map updating approach.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Wang, Xiaowei
- Wei, Qingkai
- Xie, Guotao
- Zhou, Huajian
- Sun, Ning
- Hu, Manjiang
- Publication Date: 2021-4-22
Language
- English
Media Info
- Media Type: Web
- Features: References;
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Serial:
- SAE Technical Paper
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Autonomous land vehicles; Autonomous vehicles; Digital maps; Experiments; Feasibility analysis; Forecasting; Modernization; Unpaved roads
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01773647
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2021-01-5032, 2021-01-5032
- Files: TRIS, SAE
- Created Date: May 31 2021 8:20PM