Visual Sensor Positioning via Machine Learning
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Solution Overview
Problem
The positioning of visual sensors in vehicles is traditionally fixed or manually adjustable, limiting their ability to capture environmental conditions effectively, which is a constraint for autonomous and non-autonomous vehicles relying on visual data for decision-making.
Innovation Solution
Implementing a method that uses machine learning to determine optimal sensor positions based on environmental conditions, allowing visual sensors to change position rotationally and linearly to optimize visual sensing, such as capturing images or video frames relevant to the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visual sensors use fixed or manually adjustable positioning, then device complexity is reduced, but the ability to capture environmental conditions effectively is limited
Solution Approach 1:
The patent implements dynamic positioning of visual sensors by enabling them to rotate and translate automatically based on real-time environmental conditions detected by machine learning models. The sensors transition from static fixed positions to dynamically adjustable positions, allowing optimal capture of relevant environmental information while maintaining manageable system complexity through automated control.
2Adaptability or versatility
If visual sensors are positioned manually, then ease of operation is improved, but adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The visual sensor system performs self-positioning by automatically detecting environmental conditions through machine learning models and adjusting its own position without manual intervention. The system serves itself by autonomously determining optimal positioning based on detected conditions such as traffic, pedestrians, or road features, thereby maintaining ease of operation while achieving high adaptability.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring environmental conditions through the visual sensors, processing this data through machine learning models to determine optimal positioning, and then adjusting sensor positions accordingly. This feedback mechanism enables automatic adaptation to changing conditions while eliminating the need for manual positioning control.
3Productivity
If visual sensors change position automatically based on machine learning, then productivity of visual information capture is improved, but device complexity increases
Solution Approach 1:
The visual sensor system performs multiple functions: capturing visual information for environmental perception, feeding data to machine learning models for condition detection, and automatically positioning itself based on model predictions. This multi-functionality increases productivity by integrating sensing, analysis, and actuation in one system, while managing complexity through shared hardware and software resources.
Data Source
AI summary
Systems, methods, and computer program products are provided for controlling positioning of visual sensors based on machine learned environmental conditions. One or more machine learning models are performed to determine environmental conditions based on receiving one or more visual sensor inputs. Position of one or more visual sensors are further caused to be changed in order to optimize visual sensing by the one or more visual sensors of visual information relevant to the environmental conditions.