The edge case the patent quietly admits is a budget problem: a self-driving car cannot afford to reason about everything in its view equally. US11556127B2, granted to Baidu USA (the Apollo program) in January 2023, splits the world to save effort — "Static obstacle map based perception system."
Classified under G05D 1/0088 (autonomous control), G01S 17/931 (lidar) and G06T 7/246 (object tracking), the patent's move is to build and maintain a map of the static obstacles — parked cars, walls, barriers, curbs — and treat them separately from dynamic agents. The unmoving world gets logged once; the moving world gets the expensive, continuous prediction.
Here is the pragmatism worth noting. Prediction is costly, and most of what a camera or lidar sees is not going anywhere. By segregating static structure into a map, the system frees its real-time reasoning to focus on the pedestrians, cyclists and cars that can actually surprise it. It is a compute-allocation strategy disguised as a perception architecture.
The honest risk is the misclassification at the boundary. The strategy depends on correctly sorting static from dynamic, and the dangerous failure is the parked car that suddenly pulls out — an object the system filed as static that becomes dynamic. The patent's real burden is handling exactly those transitions without being fooled.
That Baidu's Apollo program holds this is a reminder that the autonomy race is global and that Chinese AV development files heavily in the U.S. patent system. Apollo's perception choices mirror the same trade-offs Waymo and Cruise face: how to make perception fast enough to be safe on a real compute budget.
For readers comparing AV stacks, the static-versus-dynamic split is a useful lens. A stack that treats the whole scene uniformly is either compute-rich or naive; one that explicitly separates the fixed world from the moving one has confronted the budget reality that real-time driving imposes.