ANZIL: Attention-based network for zero-risk inspection of LiDAR point cloud in self-driving cars

• LiDAR point cloud contamination causes high-confidence false object detections • Graph-based network improves contaminant detection across diverse environments • Model-agnostic contaminant detector ensures reliable sensor data before processing • Cost-benefit framework assessing contaminant detection impact on perception models • Edge deployment on Jetson enables real-time processing of the contaminant detector LiDAR is widely used in autonomous vehicle perception, and its performance relies on algorithmic confidence. However, sensor contamination can lead to catastrophic mistakes in downstream tasks, such as incorrect object detections occurring with high confidence. This underscores the need for point cloud contaminant detection to identify data reliability before downstream processing. To overcome this, we propose a model-agnostic approach that integrates contaminant detection with downstream tasks, using graph representations and attention networks. Trained on real contaminated LiDAR, the contaminant detector complements any existing clean-trained downstream model. Point clouds passing through the contaminant detector are discarded if contamination is detected. We propose a novel cost-benefit methodology, evaluating contaminant detectors in downstream processing. The benefit measures the proportion of discarded frames that would have caused high-confidence errors in the downstream task. The cost includes the model misalignment cost, representing frames wrongly discarded despite being correctly processed, and the true cost, which reflects uncontaminated frames falsely identified as contaminated. The contaminant detector was tested on 78,000 frames with water, dust, mud, salt, oil, and foam contamination from tunnel and outdoor locations. It achieved an F1-score of 0.884 and a recall of 0.957 on a static dataset, and a 0.975 F1-score in unseen real-world environments, demonstrating high sensitivity. In model-agnostic evaluation, the method is applicable to any object detector and reduces catastrophic mistakes by at least 97%, successfully identifying 4,046 failure cases. Deployed on the NVIDIA Jetson AGX Xavier, it achieved inference in under 5 ms and point cloud transformation in 200 ms, making it suitable for edge processing. This enhances sensor reliability, enables automatic cleaning, and improves vehicle safety.