13-A Comparative Analysis of the Effects of Work Zone Lane Marking Materials and Marker-Painting Methods on LiDAR Point Cloud Measurements
Date and Time: Tuesday, July 30, 2024: 5:00 PM - 6:30 PM
Location: Indigo BC
Xinyu Cao
Graduate Research Assistant, Pennsylvania State University
Presentation Description
This presentation delves into the critical role of LiDAR (Light Detection and Ranging) technology in the navigation and mapping systems of autonomous vehicles (AVs). Given the importance of precise perception for safe AV operation, LiDAR stands out due to its robust performance in various illumination conditions, ensuring reliable detection day and night, even in low visibility scenarios.
The focus of this study is on the performance of LiDAR in detecting lane markings within work zones, an area characterized by a significant diversity of marking materials and methods. These variations include different paint types (with and without glass beads), lane tapes of varying colors and thicknesses, and a mix of new, worn, and covered-up markings.
To systematically evaluate LiDAR's detection capabilities, the research team conducted experiments using a LiDAR-equipped mapping vehicle within a controlled environment that mimicked real-world scenarios. Several test cases were constructed, featuring materials such as normal paint, worn paint, tape, worn tape, reflective glass bead paint, and worn reflective glass bead paint. The painting operations were performed by official state Department of Transportation (DOT) vehicles to ensure accuracy and compliance with real-world practices.
The findings of this comparative analysis reveal intricate relationships between LiDAR performance and the different lane marking materials. Key factors such as surface texture and reflectivity significantly influence LiDAR's detection capabilities. While some materials showed strong detection performance, others posed challenges, particularly under adverse weather and operational conditions.
This research contributes to the advancement of autonomous driving systems by providing insights into how various lane marking materials affect LiDAR perception. The results suggest that specific features within work zones could be leveraged to enhance AV navigation and behavior management.
Speaker Biography
Xinyu Cao is a fourth-year PhD student at Penn State University, specializing in Mechanical Engineering. With a robust expertise in sensor calibration, sensor fusion, and advanced mapping systems, Xinyu has dedicated his research to the development and enhancement of mapping technologies. His work focuses on improving the accuracy and reliability of perception and mapping through innovative approaches in sensor integration and mapping methodologies.
Presentation File
A Comparative Analysis of the Effects of Work Zone Lane Marking Materials and Marker-Painting Methods on LiDAR Point Cloud Measurements
Category
Infrastructure Technology Supporting Road Vehicle Automation