Traffic Signal Camera Calibration for Underexposure Misalignment
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Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in accurately detecting traffic signals due to miscalibration of traffic signal cameras, which can lead to incorrect identification and response to traffic signals, potentially causing safety issues during navigation.
Innovation Solution
The system processes images from traffic signal cameras to detect miscalibration by analyzing the positioning of light sources within bounding boxes and performs alternate reason checks for occlusions or misalignments, initiating recalibration actions when miscalibration is detected, including safe stops and real-time adjustments to ensure accurate traffic signal detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic signal cameras are used to detect traffic signals, then traffic signal detection capability is improved, but camera miscalibration can lead to incorrect identification and safety issues
Solution Approach 1:
The system performs preliminary calibration checks by analyzing the positioning of light sources within bounding boxes in captured images. Before making detection decisions, the system verifies that light sources are properly positioned within expected regions, preventing miscalibration issues from affecting traffic signal identification accuracy.
Solution Approach 2:
The system implements feedback mechanisms by performing alternate reason checks when miscalibration is detected. When light sources are not properly positioned within bounding boxes, the system investigates alternative explanations (such as occlusions or unusual traffic signal configurations) before concluding miscalibration, and triggers recalibration procedures to correct positioning errors.
2Measurement precision
If the system performs comprehensive calibration checks and alternate reason analysis, then detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system applies partial checking by focusing calibration verification on specific critical elements (light source positioning within bounding boxes) rather than analyzing entire images comprehensively. This selective approach maintains detection accuracy for calibration issues while reducing overall processing time and computational burden.
Solution Approach 2:
The system implements skip logic in its calibration verification process. When initial checks pass (light sources are properly positioned within bounding boxes), the system skips detailed alternate reason analysis. Alternate reason checks are only performed when miscalibration indicators are detected, allowing the system to rapidly process normally calibrated cameras while thoroughly investigating problematic cases.
Data Source
AI summary
The present technology pertains to determining whether traffic signal cameras of an AV are miscalibrated. One technique for determining if a given traffic signal camera is miscalibrated includes obtaining an image from a traffic signal camera, marking the location of a traffic signal in the image with a bounding box, and determining if the light sources of the traffic signal are within the bounding box. If not, an alternate reason check is performed to determine whether an alternate reason exists for why the light source of the traffic signal is not within the bounding box. If no such reason exists, then the traffic signal camera may be miscalibrated. An additional calibration check may be performed using images from two traffic signal cameras of the AV each having four common light sources, and performing a homographic computation using the location of the light sources in the images.