Friction Estimation Algorithm Using Camera and Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing friction estimation systems for vehicles rely on sensor measurements, which provide accurate but delayed information, making it difficult for vehicles to adapt quickly to changing road conditions, especially with camera-based methods being unreliable for timely friction updates.
Innovation Solution
A system that combines sensor-based friction estimation with camera-based inputs to adapt the friction estimation algorithm dynamically, increasing sensitivity for potential sudden changes and stability for consistent conditions, using a vehicle processing device to receive and process estimates from various sensors, including cameras, to ensure timely and accurate friction assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If friction estimation is based on sensor measurements from the vehicle, then measurement precision is improved, but loss of time increases because the information is available too late to be useful for adapting systems
Solution Approach 1:
The system performs preliminary friction estimation using camera-based methods before the vehicle actually encounters the friction change. By continuously analyzing road surface images ahead of the vehicle, the system predicts upcoming friction conditions and prepares the vehicle systems in advance, thus reducing the effective response time while maintaining accurate friction measurement through sensor validation.
2Loss of time
If camera-based friction estimation is used to provide earlier friction information, then loss of time is reduced, but reliability worsens because camera-based estimation is unlikely to be very accurate
Solution Approach 1:
The system merges two friction estimation approaches: camera-based preliminary estimation and sensor-based accurate measurement. The camera provides early warnings of friction changes, while vehicle sensors (wheel speed, acceleration) validate and refine the estimation. This combination allows the system to benefit from both the early detection capability of cameras and the reliability of physical sensors.
Solution Approach 2:
The system uses feedback from vehicle sensors to continuously validate and adjust camera-based friction estimates. When sensor measurements confirm or contradict camera predictions, the system adjusts its confidence in camera-based estimates, creating a self-correcting mechanism that improves reliability while maintaining early warning capability.
3Loss of time
If the friction estimation algorithm sensitivity is increased to detect sudden friction changes, then response time is improved, but stability worsens causing false detections on consistent friction surfaces
Solution Approach 1:
The friction estimation algorithm dynamically adjusts its sensitivity based on current driving conditions and confidence levels. When camera-based predictions indicate potential friction changes, the algorithm temporarily increases sensitivity to detect them quickly. When conditions are stable and confidence is high, sensitivity is reduced to maintain algorithm stability and avoid false detections. This dynamic adjustment allows the system to optimize between rapid detection and stability depending on the situation.
Data Source
AI summary
A system for estimating the friction between a road surface and a tire of a vehicle includes at least one first sensor and at least one vehicle processing device containing a friction estimation algorithm which is arranged to estimate the friction between the road surface and the tire of the vehicle based on friction related measurements is provided. The vehicle processing device is arranged to: receive an estimate of the expected friction between the road surface and the tire of the vehicle from a central processing device, from a storage device in the vehicle, or from at least one second sensor in the vehicle; adapt the friction estimation algorithm based on said received estimate of the expected friction; receive at least one friction related measurement from the at least one first sensor in the vehicle; and use the adapted friction estimation algorithm to perform an estimation of the friction between the road surface and the tire of the vehicle based on the at least one friction related measurement.