Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study

Range-measuring sensors play a critical role in autonomous driving systems. While Light Detection and Ranging ( LiDAR) technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions - the implications of ill-reflec...

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Bibliographic Details
Main Authors: Lotscher, Michael, Baumann, Nicolas, Ghignone, Edoardo, Ronco, Andrea, Magno, Michele
Format: Conference Proceeding
Language:English
Subjects:
Autonomous Driving
Depth-Camera
Distance measurement
Laser radar
LiDAR
RADAR
Ranging Sensors
Robot sensing systems
Robot vision systems
Robustness
Sensor fusion
Sensor systems
Time-of-Flight
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Summary:Range-measuring sensors play a critical role in autonomous driving systems. While Light Detection and Ranging ( LiDAR) technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions - the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, Radio Detection and Ranging (RADAR), and Depth-Camera. Based on accurate experimental evaluation the paper's findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas (RADAR) and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving.
ISSN:2768-1734
DOI:10.1109/WF-IoT58464.2023.10539485