Camera misalignment detection system for a vehicle
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Solution Overview
Problem
Existing camera misalignment detection algorithms are prone to false positives during limited visibility conditions such as nighttime driving and inclement weather, necessitating an improved method for robust detection.
Innovation Solution
A camera misalignment detection system that determines matched pixel pairs, calculates alignment angle differences using an essential matrix, and performs statistical filtering to identify a misalignment condition based on feature matching ratios and convergence conditions, transmitting a disablement signal to affected vehicle systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion vector-based camera misalignment detection algorithm is used, then detection capability is provided, but false positive events occur during limited visibility conditions such as nighttime driving and inclement weather
Solution Approach 1:
The patent segments the detection process into multiple independent modules: feature detection module, feature matching module, essential matrix calculation module, and misalignment determination module. Each module processes specific aspects of the detection task, allowing the system to handle limited visibility conditions by processing visual features separately from motion vectors, thereby reducing false positives while maintaining detection capability.
Solution Approach 2:
The patent merges multiple detection approaches by combining feature-based detection (using SIFT or ORB algorithms) with motion vector analysis. The system integrates results from both methods and uses the essential matrix to correlate them, creating a more robust detection system that can distinguish true misalignment from false indicators during nighttime or inclement weather conditions.
2Measurement precision
If feature matching ratio and essential matrix are used to improve detection accuracy, then false positives are reduced, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using a threshold-based approach to feature matching. Instead of processing all possible feature combinations, the system matches features only when they exceed a predefined similarity threshold, then proceeds to calculate the essential matrix only for matched pairs that pass this initial filter. This selective processing reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The patent implements continuous processing of image frames through a loop that repeatedly executes feature detection, matching, and misalignment determination on sequential frames. The system maintains a queue of historical alignment angle difference values and continuously updates statistical filters, ensuring that detection accuracy is maintained over time while managing computational load through efficient iterative processing.
Data Source
AI summary
A camera misalignment detection system detecting a misalignment condition of one or more cameras for a vehicle includes one or more controllers that determine a set of matched pixel pairs, determine a feature matching ratio based on the set of matched pixel pairs, and calculate an alignment angle difference of the one or more cameras. In response to determining the feature matching ratio, the essential matrix inlier ratio, and the alignment angle difference each exceed respective threshold values, the controllers add the alignment angle difference to a queue including a sequence of historical alignment angle difference values. The controllers perform statistical filtering to determine a total number of historical alignment angle difference values within the queue that are inliers and determine a misalignment condition of the one or more cameras based on the total number of historical alignment angle difference values within the queue that are inliers.