Road Gradient Estimation via Dynamic Sensor Fusion
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Solution Overview
Problem
Existing methods for estimating road gradient using accelerometers are unreliable on roads with significant gradients and bends due to inclusion of curvature-related accelerations, while force equation methods provide reliable but slow estimates.
Innovation Solution
A sensor fusion method that adjusts sensitivity and input signals based on detected dynamic processes, using a Kalman filter to weight accelerometer and force equation inputs, optimizing the combination of methods to disregard irrelevant acceleration components and provide quick and accurate road gradient estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerometer method is used to estimate road gradient, then estimation speed is fast, but reliability deteriorates on roads with significant gradients and bends due to curvature-related accelerations
Solution Approach 1:
The system dynamically switches between accelerometer-based estimation and force equation-based estimation based on detected driving conditions. When curvature-related accelerations are detected (indicating bends or significant gradients), the system transitions from the fast but unreliable accelerometer method to the reliable but slower force equation method, optimizing both speed and reliability across different operating conditions
Solution Approach 2:
The system changes the estimation parameters by selecting different input signals and sensitivity levels based on detected dynamic processes. The sensor fusion adjusts its parameters dynamically - using accelerometer data with high sensitivity during normal conditions, and switching to force equation parameters when curvature effects are present, thereby maintaining both speed and reliability
2Reliability
If force equation method is used to estimate road gradient, then reliability is high, but estimation speed becomes slow
Solution Approach 1:
The system employs dynamic estimation by switching between force equation-based estimation and accelerometer-based estimation depending on the driving situation. The force equation method is applied when high reliability is needed (during bends or significant gradients), while the accelerometer method is used during normal conditions to maintain fast estimation speed, thus balancing reliability and productivity
Solution Approach 2:
The sensor fusion periodically evaluates driving conditions and switches between estimation methods accordingly. Rather than continuously using the slow force equation method, the system periodically checks for curvature-related accelerations and only applies the force equation method when necessary, maintaining high reliability while minimizing the impact on estimation speed
3Measurement precision
If sensor fusion adjusts sensitivity based on dynamic processes, then estimation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the estimation process into distinct modes: accelerometer-based estimation for normal conditions and force equation-based estimation for conditions with curvature-related accelerations. This segmentation allows the system to maintain high accuracy by selecting the appropriate method for each condition while managing complexity through clear separation of estimation paths
Solution Approach 2:
The sensor fusion acts as an intermediary that detects dynamic processes and mediates between the accelerometer and force equation methods. By introducing this intermediary layer that automatically selects and weights input signals based on detected conditions, the system achieves high estimation accuracy without requiring complex manual intervention or overly complicated system architecture
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves reliable and timely estimation of road gradient and curvature changes, enhancing automatic gear choice and other vehicle systems by combining the strengths of accelerometer and force equation methods while minimizing their weaknesses.
Implementation Method 1
The invention uses a sensor fusion to jointly weight various methods for determining the gradient α which involve using an accelerometer or a force equation. On the basis of detecting the occurrence of dynamic processes, the invention adjusts this sensor fusion in such a way that the respective advantages of the accelerometer method and the force equation method are utilised while at the same time avoiding their respective disadvantages.
Data Source
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AI summary
The present invention relates to a method and a system for estimating a road gradient a by using a sensor fusion. The present invention detects whether at least one dynamic process is affecting said vehicle. Estimation of said gradient a is then conducted by means of the sensor fusion by joint weighting of at least two input signals to said sensor fusion. The at least two input signals comprise an input signal based on an accelerometer and an input signal based on at least one force equation. At least one of said at least two input signals and/or at least one weighting parameter for the sensor fusion are determined on the basis of said detection of whether said at least one dynamic process is affecting the vehicle.