Summary of Construction and Application Of Artificial Intelligence Crowdsourcing Map Based on Multi-track Gps Data, by Yong Wang et al.
Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data
by Yong Wang, Yanlin Zhou, Huan Ji, Zheng He, Xinyu Shen
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm combines AI with high-precision mapping technology to generate precise GPS data from large amounts of low-precision GPS trajectory data. The algorithm fuses multiple GPS signals to create simplified GPS trajectory descriptions, enabling a “crowdsourced update” model for collecting map data through social vehicles. This approach significantly improves data accuracy, reduces measurement costs, and optimizes storage space. The study analyzes the implementation form of crowdsourcing maps to enhance various information data in high-precision maps according to actual situations, paving the way for reasonable application in intelligent vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special technology called high-precision mapping to help cars drive themselves safely. Right now, there isn’t enough research on this topic, making it hard to use this tech effectively in self-driving cars. The researchers developed a new algorithm that can take lots of not-so-precise GPS data and turn it into super accurate information. This helps reduce costs, saves space, and makes the map more reliable. The study also looks at how people can contribute to updating these maps using their own vehicles, making the tech even better for self-driving cars. |
Keywords
» Artificial intelligence » Precision
