USING DEEP LEARNING TO IDENTIFY ROAD GEOMETRY FROM POINT CLOUDS
Number of patents in Portfolio can not be more than 2000
United States of America
Stats
-
N/A
Issued Date -
Apr 10, 2025
app pub date -
Oct 8, 2024
filing date -
Oct 8, 2024
priority date (Note) -
Published
status (Latency Note)
Importance
Abstract
Lidar produces three-dimensional point clouds. From the point clouds, objects need to be detected and tracked. For example, from the point clouds, embodiments detect what kind of objects is detected, what shape the object is, and the objects current location and trajectory. Each lidar sensor on a vehicle produces a point cloud periodically. The point cloud is input into a deep learning neural network that outputs road geometry, such as road edges and lane dividers. In some embodiments, the other network can also output information about other objects in the environment, such as other vehicles and pedestrians.
First Claim
Family
International Classification(s)
- [Classification Symbol]
- [Patents Count]
Cited Art Landscape
Patent Citation Ranking
Recipient Email Address
Recipient Email Address
Comment
Recipient Email Address
Add to Portfolio(s)
To add this patent to one, or more, of your portfolios, simply click the add button.
This Patent is in these Portfolios:
Add to additional portfolios:
Last Refreshed On:
Changes done successfully