HEAVY VEHICLE / OFF-HIGHWAY · TELEMETRY NORMALIZATION
16,908 confirmed mappings·18 OEM families·14 protocols·91% field coverage
Forge turns raw Freightliner tags into one clean, governed schema your AI agent already understands. One MCP call, no per-vehicle integration project.
Diagnostics live as raw J1939 SPNs on the CAN bus. Engine oil pressure, turbo RPM and brake air pressure are just numbered parameters until something decodes and names them.
Typical Freightliner equipment communicates over J1939 (CAN), HTTP / REST. Each firmware revision, model line and integrator names the same physical quantity differently, so every AI or analytics project pays the integration tax again, the tax that cancels 40% of industrial AI deployments before they ship.
Forge normalizes Freightliner telemetry into Forge's canonical field set, the same 366 canonical fields used across all 18 manufacturers. Point Forge at the vehicle, pass oem: "freightliner", and spindle speed is spindle_speed_rpm whether it came from a Freightliner controller or anything else on the floor.
Confirmed canonical fields your agent gets from Freightliner vehicles, drawn from Forge's 16,908-mapping corpus:
| Raw tag in | Canonical field out | Meaning | |
|---|---|---|---|
| SPN_100: 54 | → | vehicle.engine.oil_pressure: 54 | oil pressure |
| SPN_175: 99 | → | vehicle.engine.oil_temp: 99 | oil temp |
| SPN_103: 79000 | → | vehicle.turbocharger.rpm: 79000 | turbo rpm |
| SPN_1636: "P0299" | → | vehicle.dtc_description: "Turbo underboost" | active DTC |
No SDK, no per-tag mapping, no historian project. Connect the MCP server and normalize live Freightliner data:
Unknown tags never get force-mapped. Fields below the confidence floor abstain rather than guess, so your agent never acts on a bad reading.