Friction Coefficient Estimation via Sensor-Camera Blending
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Solution Overview
Problem
Current vehicle systems lack effective methods to accurately and dynamically estimate and respond to driving surface friction coefficients, which can lead to instability and loss of control, particularly on varying road surfaces.
Innovation Solution
An autonomous steering system that integrates sensors and cameras to estimate driving surface friction coefficients, assigning confidence levels to these estimates, and using a controller to generate control signals for the steering, braking, and propulsion systems to adjust vehicle behavior based on blended and conditional friction coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple friction coefficient estimation methods are used simultaneously, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides friction coefficient estimation into multiple independent modules: a first estimation module using sensor data (wheel speeds, steering angle, brake pressure) and a second estimation module using camera imagery. Each module operates independently with its own processing logic, allowing parallel execution without interference. This segmentation enables the system to maintain high measurement precision through multiple estimation pathways while managing complexity through modular architecture.
Solution Approach 2:
A confidence level calculation module acts as an intermediary between the multiple estimation methods and the final control decision. This intermediary computes confidence levels for each estimation based on current operating conditions (vehicle speed, acceleration, steering angle) and selects or blends the most reliable estimates. The intermediary mechanism协调s the outputs of multiple complex estimation methods, presenting a single reliable friction coefficient to the control system.
2Adaptability or versatility
If real-time friction coefficient updates are performed continuously, then adaptability to changing road surfaces is improved, but use of energy increases
Solution Approach 1:
The system implements periodic friction coefficient updates triggered by specific events rather than continuous processing. Updates occur periodically when vehicle dynamics parameters exceed thresholds (e.g., during acceleration, braking, or steering maneuvers) or when camera detection confidence changes. This periodic action maintains adaptability to road surface changes while significantly reducing computational energy consumption compared to continuous real-time processing.
Solution Approach 2:
The update frequency and processing intensity are dynamically adjusted based on current vehicle operating conditions. During steady-state cruising, friction coefficient updates occur less frequently, reducing energy use. During dynamic maneuvers or when road conditions are suspected to change (detected via sensor anomalies or camera re-detection), the system increases update frequency automatically. This dynamic adaptation maintains road surface responsiveness while optimizing energy consumption across different driving scenarios.
Data Source
AI summary
A number of illustrative variations may include a method of providing a reliable driving surface friction coefficient estimate.