Vehicle Camera Failsafe for Shadow and Imager Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicle vision systems using cameras face challenges in accurately detecting true positives and negatives while avoiding false positives and negatives, especially under varying environmental conditions such as weather, lighting, and camera obstructions, which can degrade machine vision performance.
Innovation Solution
A failsafe algorithm is implemented in the vehicle vision system to determine and mitigate adverse conditions affecting camera performance, including blockages, low light, glare, and calibration issues, by utilizing multiple cameras and image processing to provide reliable image data and alerts to the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If camera-based vision systems are used for object detection, then the system can provide automated detection capabilities, but false positives and false negatives increase under varying environmental conditions
Solution Approach 1:
The system implements a failsafe algorithm that continuously monitors detection confidence levels and provides feedback to adjust detection thresholds. When confidence is low or environmental conditions are adverse, the system adjusts its operation to maintain reliability, preventing false positives and negatives while preserving automated detection capabilities
Solution Approach 2:
The failsafe algorithm dynamically changes detection parameters such as confidence thresholds and sensitivity levels based on environmental conditions. By adjusting these parameters in response to lighting, weather, and obstruction conditions, the system maintains high detection accuracy across varying conditions while keeping automation intact
2Adaptability or versatility
If the system operates in adverse environmental conditions, then the system must maintain detection capability, but false positives and false negatives increase
Solution Approach 1:
The failsafe algorithm proactively identifies adverse environmental conditions such as poor lighting, weather effects, and camera obstructions before they significantly degrade detection performance. By detecting these conditions in advance and adjusting parameters preemptively, the system maintains measurement precision even when operating in adverse environments
Solution Approach 2:
The system performs preliminary assessment of environmental conditions and detection confidence levels before finalizing object detection results. This preliminary action allows the failsafe algorithm to adjust parameters and mitigate adverse effects before they cause false positives or negatives, ensuring maintained accuracy across diverse conditions
Data Source
AI summary
A method for determining a safe state for a vehicle includes disposing a camera at a vehicle and disposing an electronic control unit (ECU) at the vehicle. Frames of image data are captured by the camera and provided to the ECU. An image processor of the ECU processes frames of image data captured by the camera. A condition is determined via processing at the image processor of the ECU frames of image data captured by the camera. The condition includes a shadow present in the field of view of the camera within ten frames of image data captured by the camera or a damaged condition of the imager within two minutes of operation of the camera. The ECU determines a safe state for the vehicle responsive to determining the condition.