Tire Tread Depth Prediction Using Accelerometer Vibration Analysis

The patent replaces manual mechanical inspection methods with an automated sensor-based system. An accelerometer mounted on the tire measures vibration signals, which are then processed through signal analysis algorithms to determine tread depth. This substitution of mechanical/manual measurement with automated sensing and computational analysis resolves the contradiction by achieving high measurement precision while maintaining manageable system complexity through the use of off-the-shelf sensors and standard signal processing techniques.

The patent employs pre-trained AI/ML models that have been trained offline on labeled vibration data. During real-time operation, these pre-trained models directly process accelerometer signals to predict tread depth without requiring complex real-time training or adjustment. This preliminary action of training models in advance resolves the contradiction by enabling continuous real-time monitoring while keeping the onboard computational complexity manageable, as the heavy analytical work is performed beforehand during model training.

The patent implements periodic sampling of acceleration data at optimized intervals rather than continuous recording. The system collects vibration signals at specific time intervals that are sufficient to capture the characteristic tread wear patterns while minimizing overall power consumption. This periodic measurement approach resolves the contradiction by maintaining measurement precision through adequate sampling frequency while significantly reducing energy usage compared to truly continuous data collection.