Proactive Flicker Mitigation via Predictive Exposure Control
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Solution Overview
Problem
Vision sensing systems in mobile platforms face issues with periodic flicker and artifacts due to windshield wiper operation, leading to glare and increased latency in object detection, as the camera circuitry takes time to adjust exposure values after occlusions.
Innovation Solution
A vision sensing system that employs a camera module and an exposure lock module using machine learning to predict blackouts based on frequency analysis, holding exposure values steady during predicted blackouts to mitigate flicker and artifacts, generating enhanced frame data for improved object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If the camera circuitry maximizes exposure value in response to blackout, then the camera can capture sufficient light during occlusion, but this causes flicker and artifacts when the exposure value returns to normal
Solution Approach 1:
The system performs preliminary action by predicting blackouts before they occur using machine learning analysis of wiper patterns and historical data. By anticipating the blackout event, the system pre-adjusts exposure values and prepares the image processing pipeline, allowing seamless transition during occlusion without the need to maximize exposure reactively, thereby preventing flicker and artifacts.
Solution Approach 2:
The system implements dynamic exposure control by continuously adapting exposure values based on real-time detection of wiper blade position and predicted blackout timing. Instead of static maximum exposure during blackout, the system dynamically adjusts exposure values in response to detected wiper patterns, maintaining optimal image quality throughout the occlusion event and eliminating the flicker caused by abrupt exposure changes.
2Measurement precision
If the camera circuitry takes time to react to return of ambient light, then the exposure value can be adjusted accurately, but this reaction time introduces latency in object detection
Solution Approach 1:
The system performs preliminary action by predicting blackouts before they occur using machine learning analysis of wiper patterns and historical data. By anticipating the blackout event, the system pre-adjusts exposure values and prepares the image processing pipeline, allowing seamless transition during occlusion without the need to maximize exposure reactively, thereby preventing flicker and artifacts.
Solution Approach 2:
The system implements continuous feedback by monitoring wiper control signals, analyzing camera data for blackout patterns, and using machine learning to predict future blackouts. This feedback loop enables the system to proactively adjust exposure values before blackouts occur, eliminating reaction time delays and maintaining continuous object detection capability without latency.
3Reliability
If machine learning is used to quantify frequency of blackouts, then future blackouts can be predicted, but this increases device complexity
Solution Approach 1:
The system implements self-service by utilizing existing wiper control signals and camera data that are already present in the vehicle system. The machine learning model is trained on this readily available data to predict blackouts, eliminating the need for additional sensors or complex hardware modifications. The system serves itself by repurposing existing resources for the new function of blackout prediction.
Solution Approach 2:
The machine learning component serves multiple functions: it analyzes wiper patterns, predicts blackout timing, and provides exposure control recommendations. By making the ML system multi-functional, the patent reduces the need for separate dedicated components for each function, thereby managing device complexity while maintaining high prediction accuracy.
Data Source
AI summary
Vision sensing systems and methods that mitigate flicker and associated artifacts. The system includes a camera module that controls operation of a camera onboard a mobile platform and generates camera data therefrom, wherein the camera data comprises a sequential plurality of frames An exposure lock module is configured to: communicate with the camera module to receive the camera data; analyze the camera data to identify a blackout; utilize machine learning to quantify a frequency of blackouts in the camera data; generate a vision sensor command for anticipating future blackouts as a function of the frequency of blackouts; and supply the vision sensor command to the camera module. An enhanced exposure control module utilizes the vision sensor command to mitigate flicker and associated artifacts, thereby providing image stabilized enhanced frames.