LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability

Abstract

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples. We have also studied the effect of contaminants on the object detection task. The state-of-the-art object detection algorithms produce catastrophic errors in detection, such as failure to identify objects, detection of ghost objects, and wrong detection with high confidence. Based on the number of such catastrophic errors, we introduce a novel measure for the LiDAR data's contamination level. The results of the empirical evaluation of the effect of the contaminants on object detection motivate the necessity of further research into contaminant detection and contaminant-resilient data processing, which are all enabled by the dataset collected by this work.