Camera Lane-Marking Validation for False Positive Detection
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Solution Overview
Problem
Prior-art camera image-processing devices in vehicles are not reliable in detecting lane markings, especially in poorly maintained lanes, leading to false positives and discomfort for passengers due to untimely correction of vehicle trajectory, which can erode trust in autonomous or semi-autonomous driving systems.
Innovation Solution
A method for detecting false positives in camera image-processing devices involves determining initial and horizon discontinuities in lane marking data, comparing them to preset thresholds, and displaying a visual warning if thresholds are exceeded, to prevent temporary loss of lane detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the image-processing device detects lane markings in poorly maintained lanes, then the device can provide lane detection functionality, but false positives occur leading to untimely correction of vehicle trajectory
Solution Approach 1:
The system performs preliminary actions by computing expected marking line positions based on vehicle trajectory and lane geometry models before actual detection. These predictions are used to pre-filter and validate detected markings, ensuring that only consistent detections are accepted, thereby preventing false positives while maintaining detection capability in challenging conditions
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing detected marking lines with predicted positions based on vehicle motion and lane geometry. Discrepancies trigger validation checks and corrections, allowing the system to maintain high precision by adjusting detections based on temporal consistency and geometric constraints
2Ease of operation
If the image-processing device corrects vehicle trajectory based on detected markings, then the vehicle can stay in lane, but false positives cause untimely correction decreasing passenger comfort
Solution Approach 1:
The system computes predicted marking positions and vehicle trajectory corrections in advance based on current lane geometry and vehicle motion. By preparing expected correction values before actual detection, the system can validate detected markings against these predictions, preventing false positive corrections that would cause passenger discomfort
Solution Approach 2:
The system applies beforehand cushioning by implementing temporal smoothing and consistency checks on trajectory corrections. Detected markings are validated against multiple time steps and geometric constraints before applying corrections, cushioning against false positives that would otherwise cause abrupt or uncomfortable vehicle movements
3Speed
If the camera processes images continuously, then real-time lane detection is achieved, but computational resources are consumed reducing system efficiency
Solution Approach 1:
The system segments the image processing task by dividing it into distinct stages: prediction of marking positions based on vehicle motion, selective detection only in regions of interest, and validation against geometric constraints. This segmentation allows real-time processing by focusing computational resources only on critical areas rather than processing entire images continuously
Solution Approach 2:
The system applies partial action by performing full computational processing only when necessary - such as when validation checks fail or when detection confidence is low. For consistent, high-confidence detections, the system uses lighter validation, reducing overall computational energy consumption while maintaining real-time detection speed
Data Source
AI summary
A method detects false positives of an image-processing device of a camera located on-board a vehicle and that delivers data for modelling a marking line. The method includes: determining a first distance from the vehicle to the marking line at a first instant in time, determining a second distance from the vehicle to the marking line at a second instant, computing an initial discontinuity, in which an initial discontinuity equal to the absolute value of the difference between the first distance and the second distance is computed, and detecting a false initial-discontinuity positive, in which the initial discontinuity is compared to a predefined initial low threshold.