Self-driving systems use similarity search to detect sensor degradation by comparing real-time sensor data against a reference dataset of known good sensor outputs. The core idea is to identify when current sensor readings deviate significantly from expected patterns, which could indicate issues like dirt accumulation, mechanical wear, or environmental interference. This approach relies on converting sensor data (e.g., camera images, LiDAR point clouds) into numerical representations (embeddings) and measuring their similarity to stored examples. If the distance between current and reference data exceeds a predefined threshold, the system flags potential degradation for further inspection or recalibration.
For example, a camera sensor’s image might be processed into a feature vector using a convolutional neural network (CNN). This vector captures essential visual patterns like edges, shapes, or object positions. The system then queries a database of feature vectors from historical “clean” images captured under similar conditions (e.g., daytime, clear weather). If the current image’s vector is significantly less similar to these references—measured by cosine similarity or Euclidean distance—the system infers the camera might be compromised. Similarly, LiDAR sensors could use geometric features (e.g., point density, object distances) to compare real-time scans against expected patterns for a given location. A sudden drop in point density in a well-mapped area, for instance, might indicate obstructed or failing hardware.
Implementation-wise, developers often use optimized libraries like FAISS or Annoy to perform fast similarity searches across large datasets. These tools enable real-time comparisons by indexing reference data into structures like k-d trees or hierarchical navigable small world graphs. For robustness, the reference dataset must account for environmental variability (e.g., lighting, weather) to avoid false positives. Some systems also update the reference dataset incrementally, using validated sensor data to adapt to long-term environmental changes while excluding outliers caused by degradation. Thresholds for similarity scores are typically calibrated using historical failure data, balancing sensitivity to degradation with tolerance for normal sensor noise. This method provides a scalable way to monitor sensor health without requiring explicit rules for every possible failure mode.