vTPMS: How Software Replaces Hardware Tire Pressure Sensors
An in-depth look at COMPREDICT's vTPMS — from AI calibration methodology and validation results to regulatory compliance and deployment architecture.
The Problem with Conventional TPMS
Traditional indirect TPMS systems deliver a binary output: a warning light when pressure deviation exceeds a threshold. Drivers learn that something is wrong, but not which tire, not by how much, and not whether it is safe to continue driving. Direct TPMS hardware solves this — but also adds in-wheel sensors, battery degradation risk, installation cost, and maintenance complexity at scale.
COMPREDICT's vTPMS eliminates that tradeoff entirely. Using only CAN signals already present in series production vehicles — wheel speed, acceleration, chassis data — its AI-calibrated model delivers absolute pressure values per wheel, continuously, without any additional hardware.
What the vTPMS Delivers
Absolute pressure per wheel
Not just a deviation warning — exact pressure values for each tire, enabling drivers to assess severity and identify the affected wheel.
Zero maintenance
No in-wheel batteries to replace, no sensor corrosion, no false warnings from hardware degradation. The system runs entirely in software.
AI-driven calibration
Accounts for temperature, load, tire wear, and weather via AI calibration and user-triggered recalibration to sustain accuracy over the vehicle's lifetime.
UNR 141-02 compliant
Successfully validated against puncture, diffusion, and malfunction test scenarios defined in the regulatory standard.
Flexible deployment
Embeds in classic AUTOSAR ECUs such as ABS controllers, or deploys on automotive-grade platforms including NXP hardware.
Digital service ready
Pressure data feeds directly into connected apps, predictive maintenance platforms, and service diagnostics.
Validation Results — Preview
Testing was conducted across a range of real-world driving scenarios. The vTPMS demonstrated mean absolute errors comparable to hardware direct TPMS sensors across all four wheels under standard driving conditions.
Puncture detection was achieved within the regulatory timeframe defined by UNR 141-02. Slow diffusion events — gradual pressure loss over extended periods — were reliably detected without false positives from normal temperature-induced pressure variation.
The system was also validated under loaded and unloaded conditions, on varied road surfaces, and across the seasonal temperature range. AI recalibration triggered by driver input successfully compensated for tire changes and seasonal pressure adjustments, restoring baseline accuracy within...
Access the Full Whitepaper
The complete whitepaper includes full validation data, accuracy benchmarks against hardware sensors, regulatory test results, integration architecture diagrams, and a deployment overview.
What's Inside the Whitepaper
Technology overview
How the vTPMS infers absolute pressure from CAN data using physics-based and AI models
Calibration methodology
AI-based calibration pipeline, user-triggered recalibration, and handling of external variables
Validation results
Full accuracy benchmarks, puncture and diffusion test data, comparison against hardware sensors
Regulatory compliance
UNR 141-02 test scenarios passed, including puncture, diffusion, and malfunction cases
Integration architecture
AUTOSAR ECU deployment, NXP hardware implementation, and CAN signal requirements
