AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection Using Graph Attention Networks

Extreme conditions and the integrity of LiDAR sensors influence AI perception models in autonomous vehicles. Lens contamination caused by external particles can compromise LiDAR object detection performance. Automatic contaminant detection is important to improve reliability of sensor information propagated to the user or to object detection algorithms. However, dynamic conditions such as variations in location, distance, and types of objects around the autonomous vehicle make robust and fast contaminant detection significantly challenging. We propose a method for contaminant detection using voxel-based graph transformation to address the challenge of sparse LiDAR data. This method considers LiDAR points as graph nodes and employs a graph attention layer to enhance the accuracy of contaminant detection. Additionally, we introduce cross-environment training and testing on real-world contaminant LiDAR data to ensure high generalization across different environments. Compared with the current state-of-the-art approaches in contaminant detection, our proposed method significantly improves the performance by as much as 0.1575 in F1-score. Consistently achieving F1 scores of 0.936, 0.902, and 0.920 across various testing scenarios, our method demonstrates robustness and adaptability. Requiring 128 milliseconds on a AMD EPYC 74F3 CPU for the end-to-end process, our method is well-suited for an early warning system, outperforming human reaction times, which require at least 390 milliseconds to detect hazards. This significantly contributes to enhancing safety and reliability in the operations of autonomous vehicles.