Vehicle Speed Prediction Using Buffer Module and Inflection Points
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Solution Overview
Problem
Conventional vehicle speed determination systems using transmission output shaft speed (TOSS) sensors are prone to noise, especially at low speeds, leading to inaccurate measurements and increased costs when alternative sensors are implemented to improve accuracy.
Innovation Solution
A system and method that determine and store changes in measured vehicle speed, predicting the speed based on an average of stored changes when the measured speed is below a threshold, using a buffer module and inflection point detection to reset and recalibrate the prediction, thereby reducing noise susceptibility and maintaining accuracy without the need for more complex sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If TOSS sensor is used to determine vehicle speed, then the system is simple and cost-effective, but the measurement precision deteriorates at low speeds due to noise
Solution Approach 1:
The system performs preliminary actions by storing historical speed change data in a buffer before prediction is needed. When current speed measurement is below the threshold, the pre-stored historical data is used to predict the current speed, avoiding the need for complex alternative sensors while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary prediction mechanism that mediates between the noisy direct TOSS sensor measurement and the final speed output. When measurements are unreliable (below threshold), the prediction module acts as an intermediary to provide accurate speed information without requiring more complex sensing hardware.
2Measurement precision
If alternative sensors are implemented to improve low-speed measurement accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
Instead of using alternative physical sensors, the system creates a virtual copy of speed information through prediction algorithms. The prediction module generates speed values based on historical patterns, effectively copying the desired accurate speed information without the need for additional or more complex physical sensing hardware.
Solution Approach 2:
The system changes the parameter being measured from direct instantaneous speed to speed change patterns over time. By analyzing how speed changes rather than measuring absolute speed directly, the system can accurately determine low-speed conditions using the same simple TOSS sensor, avoiding the need for alternative sensors.
Data Source
AI summary
A system for a vehicle includes a speed determination module, a buffer module, and a speed prediction module. The speed determination module determines changes in measured vehicle speed. The buffer module stores the determined changes in measured vehicle speed. The speed prediction module predicts a speed of the vehicle when the measured vehicle speed is less than a predetermined threshold, wherein the predicted vehicle speed is based on an average of the stored changes in measured vehicle speed.