The interesting filings approach a crowded problem from an unexpected side, and ClearMotion's application US20220281456A1, "Systems and methods for vehicle control using terrain-based localization" (published September 8, 2022), does exactly that. Most localization IP leans on GNSS, lidar maps, or visual landmarks. This one leans on the terrain itself — using the road-surface profile the vehicle senses as a positioning signal. The CPC sits in vehicle-dynamics classes (B60W 30/18163, B60W 40/06 road-condition, B60W 50/16) rather than the usual G01C mapping classes, which is the giveaway.
That class placement is consistent with ClearMotion's identity as a suspension-and-dynamics company: it already senses the road surface for active-suspension reasons, so reusing that signal for localization is a natural, defensible extension. The enabling content is the matching method — turning a sensed terrain profile into a position estimate. Where a profile is distinctive, this could complement GNSS in tunnels or urban canyons where satellite signals fail.
The skeptic's caveats are real, though. Terrain-based localization is only as good as the terrain's distinctiveness and the freshness of the reference profile; resurfaced roads, snow, and smooth highways all erode the signal. The operational design domain implied by the claim is therefore narrower than the broad independent recitation suggests. As an A1 application, the granted scope will likely be confined to specific matching or fusion methods, and prior-art tension with map-matching techniques is foreseeable.
The enabling-versus-aspirational verdict: enabling as a complementary signal, aspirational as a standalone localizer. What the September 2022 filing tells a portfolio watcher is that ClearMotion is extending its road-sensing IP into the autonomy stack from an angle few competitors hold. Label it an application, respect the ODD limits, and read the dependents — the defensible novelty is the terrain-matching method, not localization in general.