On-Board Camera Self-Calibration Using Lead Vehicle Geometry
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Solution Overview
Problem
Existing vehicle camera calibration methods require manual intervention and specialized technical knowledge, making them impractical for drivers to accurately calibrate camera systems without a fixed pattern or specialized environment.
Innovation Solution
A system-level solution using computer vision and image processing to automatically estimate camera orientation and height from the ground plane, utilizing known vehicle geometry and image processing techniques to calculate distances and update camera calibration without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used with fixed patterns, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system performs self-calibration by automatically detecting lead vehicles and using their known geometry to compute camera parameters without human intervention. The calibration process serves itself by utilizing readily available environmental objects (vehicles) rather than requiring specialized calibration equipment or expert operation.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computer vision-based system. Instead of physically positioning calibration patterns and manually adjusting camera parameters, the system uses image processing algorithms to automatically detect vehicles and compute calibration data from their geometric features.
2Manufacturing precision
If manual calibration with fixed patterns is used, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The calibration system is self-activating and requires no external calibration equipment or specialized manufacturing processes. The system automatically calibrates itself during normal operation by detecting lead vehicles, eliminating the need for separate calibration manufacturing steps or specialized environments.
Solution Approach 2:
The calibration system uses lead vehicles that serve dual purposes: they are both normal traffic objects and calibration targets. This universal approach allows the same vehicle to function as both a road user and a calibration reference, eliminating the need for specialized calibration equipment or procedures.
3Extent of automation
If automatic calibration using lead vehicle geometry is implemented, then extent of automation is improved, but measurement precision may worsen
Solution Approach 1:
The system uses any available lead vehicle as a calibration target, treating it as a temporary, disposable resource. The calibration process does not require permanent or specialized calibration equipment - any vehicle in front can serve as the reference object, and the system moves on to the next vehicle when needed.
Solution Approach 2:
The system dynamically adjusts calibration computations based on the detected vehicle's geometry and distance. By changing calibration parameters according to the specific lead vehicle detected (different vehicle types, distances, and geometries), the system maintains precision while fully automating the process.
Data Source
AI summary
Methods and systems are provided for calibrating an on-board camera of a vehicle by detecting one or both of a lead vehicle geometry and a road lane width. In one example, a method includes identifying a known vehicle geometry of a lead vehicle; estimating a distance to the lead vehicle based on matching the known vehicle geometry to an image of the known vehicle geometry from the camera; and updating a calibration of the camera based on the estimated distance.