Atlas
Returns the scenarios from real driving, ranked by how dangerous they got, so validation stops depending on which roads your fleet happened to cover.
Validate on real scenarios
A platform decision taken this year rides in vehicles until the next generation replaces it. The events that will test that decision are the ones your validation program was least likely to drive into.
Validating an ADAS or AV feature against scenarios your test fleet never encountered
Deciding which markets to launch in, and knowing whether a region is genuinely riskier or simply busier
Filling a known gap in a validation set without commissioning a new collection program
Bringing third-party evidence to a homologation or regulatory submission, rather than internal numbers
Choosing a prediction layer that fits the compute already budgeted in the vehicle
Settling an internal disagreement about how often a specific situation actually occurs
Returns the scenarios from real driving, ranked by how dangerous they got, so validation stops depending on which roads your fleet happened to cover.
Validate on real scenariosScores readiness independently, which is the difference between a number your own team produced and one a regulator will read.
Score readiness independentlySeparates a risky launch region from a busy one.
Rank launch regions by riskRuns on the hardware already in the vehicle, so the prediction layer you validated against real driving is one that can actually ship.
Ship prediction at the edgeDelivers the prediction as an in-cab warning while the driver still has time to act, which is where a validated model finally proves out.
Warn the driver in timeBy embedding Empiric Earth safety models into the chipset, automakers and ADAS providers can create a single system that evaluates driver behaviors and the exterior driving environment in real-time to predict and prevent collisions.
Learn how Empiric Earth can help address your program’s needs.
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