Path-planning claims age fast, so a serious AV company files the same idea more than once. Gatik AI did exactly that in 2022, with three grants — US11307594B2, US11320827B2 and US11487296B2 — all sharing the title "Method and system for deterministic trajectory selection based on uncertainty estimation for an autonomous agent."
The substance is in two words: deterministic and uncertainty. Classified under G05D 1/0221 (trajectory control) and G06N 20/00 (machine learning), the patents describe a vehicle that picks a path not just from what it perceives, but from how confident it is in that perception. When uncertainty is high, the deterministic selection biases toward caution — the car that is unsure slows and widens its margins.
This is the honest engineering answer to a deep problem: a learned perception system does not just output 'there is a pedestrian,' it outputs a probability, and a driving policy that ignores the confidence is brittle. Tying trajectory choice to uncertainty estimation is how you keep the vehicle conservative exactly when it has the least reason to be aggressive.
The 'deterministic' half matters for a different reason: regulators and safety cases hate randomness. A trajectory selector that, given the same uncertainty, always makes the same choice is auditable in a way a stochastic one is not. Gatik's framing is built for the certification conversation, not just the technical one.
Filing three near-identical patents in one year is a strategy, not redundancy. It builds a thicket of overlapping claims around the company's core decision layer — the trajectory selector that is, for a middle-mile freight AV like Gatik's, the actual product. The repetition is defensive depth.
For readers tracking the AV field beyond the robotaxi headliners, Gatik is a reminder that fixed-route middle-mile freight is a real, narrower autonomy market — and that its IP fights are fought over exactly this uncertainty-aware decision logic, not over flashy perception demos.