Similarity search improves black-box explainability in self-driving systems by enabling comparisons between real-time decisions and historical scenarios stored in a database. When a self-driving car makes a decision—like braking suddenly or changing lanes—the system retrieves similar past situations to contextualize why a specific action was chosen. This approach works by converting sensor inputs (e.g., camera images, LiDAR data) into embeddings (numeric representations) and searching a precomputed dataset for matches. By showing developers “this decision aligns with how the system handled similar cases before,” it bridges the gap between opaque neural network outputs and human-interpretable reasoning.
For example, suppose a car swerves to avoid an object on the road. A similarity search could surface past instances where the same object (e.g., a fallen tree branch) triggered similar evasive maneuvers. Developers can inspect these cases to verify if the decision was consistent with safe behavior or if it reveals edge cases the model struggles with. This method also helps identify patterns in ambiguous scenarios. If the car frequently misclassifies shadows as obstacles in low-light conditions, analyzing retrieved matches might show recurring false positives in similar lighting, guiding engineers to improve training data or sensor fusion algorithms. Tools like k-nearest neighbors (KNN) or approximate nearest neighbor (ANN) libraries are often used to efficiently search large datasets of driving scenarios.
Beyond debugging, similarity search builds trust in the system’s behavior. For instance, during validation, if a car slows down unexpectedly, engineers can review matched scenarios to confirm the action was justified (e.g., detecting a pedestrian partially occluded by a parked car). This transparency is critical for regulatory compliance and stakeholder confidence. However, the quality of explanations depends on the dataset’s diversity and the embedding method’s ability to capture relevant features. If the dataset lacks rare scenarios (e.g., extreme weather), similarity search might fail to explain decisions in those cases. Overall, it’s a pragmatic way to map complex model outputs to observable, real-world contexts without requiring full model interpretability.