Self-driving cars can use similarity search to detect unseen attack patterns by comparing real-time sensor data or system behaviors against a database of known normal and malicious scenarios. Similarity search works by converting data into high-dimensional vectors (embeddings) and measuring how “close” new inputs are to existing entries using metrics like cosine similarity or Euclidean distance. If a new input closely matches a known attack signature or deviates significantly from normal patterns, the system flags it as suspicious. This approach doesn’t require prior knowledge of every possible attack—instead, it identifies anomalies by finding patterns that are “similar but different enough” to raise concern.
For example, consider adversarial attacks on a car’s camera system. An attacker might place stickers on a stop sign to trick the car into misclassifying it. A similarity search system could store embeddings of both clean and manipulated stop signs. When the car encounters a new sign, it converts the image into an embedding and searches for the nearest matches in its database. If the closest matches include manipulated signs (even if the exact sticker pattern isn’t identical), the system could trigger a safety protocol, like slowing down or alerting a human operator. Similarly, for LiDAR spoofing attacks—where false objects are projected into the sensor’s field—the system could compare point cloud data against known spoofed patterns, flagging anomalies that resemble past attacks but vary in details like object shape or density.
Implementing this requires balancing accuracy and speed. Self-driving systems need real-time responses, so approximate nearest neighbor (ANN) algorithms like FAISS or HNSW are often used to reduce computational overhead. The database must also be continuously updated with new attack patterns observed in the wild or simulated during testing. One challenge is avoiding false positives: unusual but benign scenarios (e.g., a graffiti-covered stop sign) shouldn’t trigger alarms. To address this, developers might combine similarity search with secondary checks, such as cross-verifying with other sensors (radar, GPS) or using statistical thresholds to filter outliers. This layered approach ensures the system remains robust without compromising performance.