Self-driving cars use vector search as part of their localization systems to mitigate GPS signal interference by cross-referencing real-time sensor data with pre-mapped environments. GPS signals can be disrupted by physical obstructions like buildings, tunnels, or weather, or by intentional jamming. To maintain accurate positioning, autonomous vehicles combine GPS with other sensors (e.g., lidar, cameras, inertial measurement units) and high-definition (HD) maps. Vector search enables the car to compare live sensor data—represented as mathematical vectors of features like road signs or curb positions—against a stored vectorized map. This process identifies the closest match between observed data and the map, correcting GPS errors.
The core mechanism involves converting sensor inputs into vector embeddings that describe the car’s surroundings. For example, lidar scans generate point clouds that are processed into geometric features (e.g., distances to lane markings or building corners). These features are structured as vectors and compared to an HD map’s vector database using algorithms like nearest-neighbor search. If GPS claims the car is at coordinates X, but lidar and camera data align more closely with map vectors at coordinates Y, the system prioritizes the sensor-map match. This approach relies on efficient vector similarity metrics, such as Euclidean distance or cosine similarity, to quickly identify the most plausible location. Frameworks like FAISS or specialized embedded databases optimize these searches for low latency, which is critical for real-time navigation.
A practical example occurs in urban areas with “GPS shadows” caused by tall buildings. Here, a self-driving car might detect a specific arrangement of traffic lights and storefronts via cameras. Vector search matches these features to the HD map, overriding the GPS’s drifted coordinates. Similarly, in tunnels where GPS fails entirely, the car uses inertial measurement unit (IMU) data—tracking acceleration and wheel rotations—to estimate movement vectors. These vectors are continuously compared against the map’s expected road geometry, ensuring the car stays on course until GPS resumes. By fusing vector search with multi-sensor inputs, autonomous systems maintain robust positioning even when GPS is unreliable, reducing reliance on any single data source.