Multi-Camera Alignment Compensation for Uneven Road Surfaces
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Solution Overview
Problem
Uneven road surfaces degrade the performance of spatial monitoring and autonomous vehicle control systems by affecting camera alignment, which is critical for advanced driver assistance systems and autonomous vehicle functions.
Innovation Solution
A vehicle-mounted spatial monitoring system using multiple cameras and a controller to detect uneven road surfaces by determining ground plane normal vectors and angle differences, and dynamically adjusting camera alignment to generate a bird's eye view image, enabling autonomous control of steering, acceleration, or braking systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used for spatial monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses the cameras themselves to detect road surface unevenness and calculate alignment compensation factors. The spatial monitoring system automatically identifies ground plane normal vectors from captured images and computes the necessary compensation without external intervention, making the system self-calibrating and reducing operational complexity despite having multiple cameras
Solution Approach 2:
The system dynamically adjusts camera alignment parameters by applying compensation factors to camera mounting angles based on detected road surface conditions. This allows the system to maintain measurement precision across varying road surfaces by changing the operational parameters (camera angles) rather than the physical structure
2Reliability
If camera alignment is dynamically adjusted to compensate for uneven road surfaces, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements a feedback loop where captured images are analyzed to detect road surface unevenness, which then triggers calculation of alignment compensation factors that are applied to adjust camera mounting angles. This closed-loop feedback mechanism ensures reliable spatial monitoring under varying road conditions while automating the adjustment process to manage complexity
Solution Approach 2:
The system pre-calculates and applies alignment compensation factors before spatial monitoring is critically affected by road surface unevenness. By detecting unevenness early and proactively adjusting camera angles, the system maintains reliability without requiring complex real-time intervention mechanisms
3Measurement precision
If ground plane normal vectors are calculated from multiple images, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system calculates ground plane normal vectors using images from only two cameras (front and side) rather than all available cameras, providing sufficient precision for alignment compensation without the computational overhead of processing all images. This partial action approach balances measurement precision with processing time efficiency
Solution Approach 2:
The processing is divided into distinct segments: first detecting road surface unevenness from image data, then calculating ground plane normal vectors, and finally computing alignment compensation factors. This segmentation allows the system to perform only the necessary calculations for the current task, reducing overall processing time while maintaining precision
Data Source
AI summary
A vehicle control system including a spatial monitoring system includes on-vehicle cameras that capture images, from which are recovered a plurality of three-dimensional points. A left ground plane normal vector is determined for a left image, a center ground plane normal vector is determined for a front image, and a right ground plane normal vector is determined for a right image. A first angle difference between the left ground plane normal vector and the center ground plane normal vector is determined, and a second angle difference between the right ground plane normal vector and the center ground plane normal vector is determined. An uneven ground surface is determined based upon one of the first angle difference or the second angle difference, and an alignment compensation factor for the left camera or the right camera is determined. A bird's eye view image is determined based upon the alignment compensation factor.