View source for 18047102. PHYSICS-BASED MODELING OF RAIN AND SNOW EFFECTS IN VIRTUAL LIDAR simplified abstract (GM Global Technology Operations LLC)

=PHYSICS-BASED MODELING OF RAIN AND SNOW EFFECTS IN VIRTUAL LIDAR=


==Organization Name==

[[:Category:GM Global Technology Operations LLC|GM Global Technology Operations LLC]]


[[Category:GM Global Technology Operations LLC]]

==Inventor(s)==

[[:Category:Wei Zeng of Oakland Township MI (US)|Wei Zeng of Oakland Township MI (US)]][[Category:Wei Zeng of Oakland Township MI (US)]]

[[:Category:Shuqing Zeng of Sterling Heights MI (US)|Shuqing Zeng of Sterling Heights MI (US)]][[Category:Shuqing Zeng of Sterling Heights MI (US)]]

[[:Category:Huabing Zhu of Troy MI (US)|Huabing Zhu of Troy MI (US)]][[Category:Huabing Zhu of Troy MI (US)]]

[[:Category:Jordan B. Chipka of Commerce Twp. MI (US)|Jordan B. Chipka of Commerce Twp. MI (US)]][[Category:Jordan B. Chipka of Commerce Twp. MI (US)]]

==PHYSICS-BASED MODELING OF RAIN AND SNOW EFFECTS IN VIRTUAL LIDAR - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18047102 titled 'PHYSICS-BASED MODELING OF RAIN AND SNOW EFFECTS IN VIRTUAL LIDAR

The patent application describes a method for modeling precipitation effects in a virtual LiDAR sensor. 

* Receives a point cloud model representing three-dimensional coordinates of objects as sensed by a LiDAR sensor.
* Generates a stochastic model of rainfall or snowfall.
* Estimates the probability of a light source from the LiDAR sensor hitting a raindrop or snowflake based on the stochastic model.
* Modifies the point cloud model to include effects induced by the modeled rainfall or snowfall based on the probability of encountering a raindrop or snowflake.

Potential Applications:
- Weather forecasting
- Environmental monitoring
- Autonomous driving systems

Problems Solved:
- Enhances the accuracy of LiDAR sensor data in adverse weather conditions
- Improves the performance of LiDAR-based systems in rain or snow

Benefits:
- Increased reliability of LiDAR sensor data
- Enhanced safety in various applications
- Better decision-making based on more accurate environmental data

Commercial Applications:
Title: "Enhanced LiDAR Sensor for Adverse Weather Conditions"
This technology can be utilized in autonomous vehicles, meteorological research, and environmental monitoring systems, among others. The market implications include improved safety and efficiency in various industries.

Prior Art:
There may be prior art related to modeling precipitation effects in LiDAR sensors, but specific information is not provided in the abstract.

Frequently Updated Research:
Stay updated on advancements in LiDAR sensor technology, weather modeling, and environmental monitoring to enhance the effectiveness of this innovation.

Questions about Modeling Precipitation Effects in a Virtual LiDAR Sensor:

Question 1: How does the method of estimating the probability of light source interaction with raindrops or snowflakes improve LiDAR sensor performance in adverse weather conditions?
Answer 1: By accurately modeling precipitation effects, the LiDAR sensor can adjust its readings to account for interference caused by rain or snow, leading to more reliable data in challenging weather scenarios.

Question 2: What are the potential challenges in implementing this technology across different industries and applications?
Answer 2: Some challenges may include calibrating the LiDAR sensor to different precipitation types, ensuring real-time processing capabilities, and integrating the modified point cloud data effectively into existing systems.


==Original Abstract Submitted==

A method of modeling precipitation effects in a virtual LiDAR sensor, the method includes receiving a point cloud model representing three-dimensional coordinates of objects as the objects would be sensed by a LiDAR sensor. The method further includes generating a stochastic model of rainfall or snowfall, estimating a probability that a light source from the LiDAR sensor hits a raindrop or a snowflake based on the stochastic model, and modifying the received point cloud model to include effects induced by the modeled rainfall or snowfall based on the probability that light sourced from the LiDAR sensor encounters a raindrop or a snowflake.



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