Surround View Monitor Calibration Using Driving State Triggers
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Solution Overview
Problem
Existing vehicle systems face challenges in efficiently and reliably calibrating surrounding images due to changes in camera position or field of view caused by assembly tolerance or vibration, requiring frequent and resource-intensive calibration checks during driving.
Innovation Solution
A vehicular surrounding image calibration method that utilizes a connected car service to determine driving lane and vehicle speed control activation, assesses road and environmental conditions, and performs automatic calibration of the surround view monitor function when conditions are met, employing multiple controllers and sensors for efficient image synthesis and calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If calibration is performed constantly by checking multiple strict conditions during driving, then the reliability of surrounding image calibration is improved, but the load on the controller increases
Solution Approach 1:
The system uses the vehicle's existing driving state information (from lane keeping assist and cruise control systems) and environmental sensor data to automatically determine calibration suitability, eliminating the need for separate manual calibration operations. The calibration process is triggered automatically when the system determines conditions are appropriate, reducing controller burden while maintaining calibration reliability.
Solution Approach 2:
The system pre-establishes calibration criteria by defining specific driving states (lane keeping assist and cruise control activation) and environmental conditions (luminance, weather, road type) that indicate suitable calibration opportunities. By preparing these criteria in advance, the system can quickly assess whether calibration is needed without performing complex real-time analysis, thereby reducing controller load during operation.
2Manufacturing precision
If multiple strict conditions are checked before performing calibration during driving, then the quality of calibration is improved, but the time required for condition determination increases
Solution Approach 1:
The system leverages existing vehicle systems (lane keeping assist, cruise control, environmental sensors) that serve multiple functions. These systems already collect and process data for their primary purposes, and the calibration trigger mechanism reuses this existing data infrastructure. This multi-functional approach allows the system to evaluate multiple calibration conditions simultaneously using data already being collected, reducing the time required for condition determination while maintaining comprehensive calibration quality checks.
3Measurement precision
If calibration is performed frequently to compensate for camera position changes, then the accuracy of surround view monitor is improved, but the energy consumption increases
Solution Approach 1:
Instead of continuous calibration checks, the system implements periodic calibration triggered by specific driving states and environmental conditions. The calibration is performed only when the vehicle is in stable driving conditions (lane keeping assist and cruise control active) and environmental factors are favorable, creating a periodic rather than continuous calibration regime. This approach maintains surround view accuracy by performing calibration at appropriate intervals while significantly reducing energy consumption compared to frequent or continuous calibration attempts.
Data Source
AI summary
A vehicular surrounding image calibration method may include determining whether driving lane and vehicle speed control functions are activated, upon determining that the driving lane and vehicle speed control functions are activated, determining a satisfied or dissatisfied state of each of a road condition and an external environmental condition based on a connected car service (CCS), and upon determining that the external environmental condition and the road condition are satisfied, performing automatic calibration of a surround view monitor (SVM) function.