Self-driving cars use similarity search to recognize emergency situations by comparing real-time sensor data to pre-identified patterns of critical events stored in their databases. When a vehicle’s sensors (cameras, LiDAR, radar) detect an object or scenario, the system encodes the data into a high-dimensional vector representation. This vector is then queried against a library of known emergency scenarios—like sudden braking, pedestrians darting into the road, or obstacles appearing unexpectedly—using similarity search algorithms. If the current scenario closely matches an emergency pattern, the car triggers appropriate safety protocols, such as emergency braking or evasive maneuvers.
For example, consider a scenario where a child runs into the street chasing a ball. The car’s sensors capture the movement and spatial context, converting it into a vector. The system then searches its database for vectors representing similar trajectories (e.g., sudden lateral movement near a crosswalk). If the similarity score exceeds a threshold, the car identifies it as an emergency and reacts. This process relies on machine learning models trained to encode scenarios into vectors that emphasize critical features like speed, direction, and proximity. Techniques like k-nearest neighbors (k-NN) or approximate nearest neighbor (ANN) search enable fast comparisons, even with large datasets.
However, implementing this effectively requires balancing speed and accuracy. Self-driving systems often use optimized ANN libraries (e.g., FAISS or HNSW) to handle real-time queries across millions of stored vectors. Edge cases—like rare emergency scenarios not well-represented in training data—remain a challenge. To address this, developers continuously update scenario databases with new data from real-world driving and simulations. For instance, if a car encounters an overturned truck partially blocking a highway, the system might not initially recognize it. But once logged and labeled as an emergency, future vehicles can match similar obstructions faster. This iterative process ensures the system improves over time while maintaining low latency for split-second decisions.