Impact of Fog Particles on 1.55 μm Automotive LiDAR Sensor Performance: An Experimental Study in an Enclosed Chamber University of Minnesota-Twin Cities

Format:

Book

Conference/Event

Author/Creator:

Zhan, Lu, author.

Contributor:

Northrop, William F.

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

To achieve full automation in self-driving vehicles, environmental perception sensing accuracy is critically important. However, ambient particles in adverse weather like foggy, rainy, or snowy conditions can significantly scatter the incident laser beam, and therefore contaminate the intensity and accuracy of light detection and ranging (LiDAR) sensors. Especially compared to the rapidity of technology development in self-driving vehicles, there is a significant lack of documented research on LiDAR systems with wavelength longer than 1 μm for application in Advanced Driver-Assistance Systems. In this work, experimental studies were performed with a state-of-the-art 1.55 μm wavelength automotive-grade LiDAR system in a controlled laboratory fog chamber. The goal of the research is to correlate laser attenuation and the optical properties of fog particles. In this work, a thorough multistep procedure for LiDAR data analysis is presented including spatial averaging of the object measurement and characterizing the temperature effect on a LiDAR intensity parameter. Fog particle density is measured by a commercial visibility sensor instrument. Assuming a constant extinction coefficient and backscatter coefficient, a simple analytical model is derived that correlates LiDAR reflectance and extinction coefficient measured by visibility sensor. Results show that the correlation coefficient between LiDAR and visibility sensor data is 0.98 and the R-squared value of linear fitting is 0.96. By comparing the LiDAR original signal and the model, the Root-Mean-Squared Deviation is 0.007, meaning the model performs very well for predicting LiDAR reflectance in the controlled environment. Furthermore, although the returned signal strength is attenuated, the LiDAR can measure the target with a visibility range lower than six meters

Notes:

Vendor supplied data

Publisher Number:

2021-01-0081

Access Restriction:

Restricted for use by site license