Overlapping Vehicle Cameras for Position Change Detection
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Solution Overview
Problem
Current systems for detecting position changes of cameras or sensors on moving vehicle parts are inadequate as they fail to provide timely and accurate information, leading to inaccurate sensor data in advanced driver assistance systems (ADAS) and automated driving processes.
Innovation Solution
A system comprising multiple sensors with overlapping fields of view, controlled by a processor executing programmatic control logic that includes acquiring overlapping optical information, computing conditional correspondence probability distributions, and dynamically aligning sensors to ensure accurate calibration and position detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensors are mounted on movable vehicle components to enable ADAS functions, then the system can provide comprehensive environmental perception, but the sensor position may change due to impact or automated movement failure, leading to inaccurate sensor data
Solution Approach 1:
The system performs preliminary calibration of sensors mounted on movable vehicle components during manufacturing or setup. This pre-calibration establishes baseline position data that can be used to detect subsequent position changes, allowing the system to maintain measurement precision even when sensors are placed on movable parts for adaptability
Solution Approach 2:
The system continuously monitors sensor position by comparing current sensor data against reference data from properly aligned sensors. When position changes are detected through this feedback mechanism, the system can trigger alerts or recalibration procedures, maintaining measurement accuracy despite the flexibility of mounting sensors on movable components
2Measurement precision
If the system continuously monitors sensor position to detect changes timely, then measurement accuracy is maintained, but computational burden and system complexity increase
Solution Approach 1:
Instead of continuously analyzing all sensor data at full computational capacity, the system performs position monitoring only when necessary or at reduced intervals during normal operation. This partial monitoring approach maintains measurement precision while significantly reducing computational burden and system complexity compared to continuous full-scale analysis
Solution Approach 2:
The system introduces a reference sensor or reference data set as an intermediary element. By comparing current sensor positions against this pre-established reference, the system can detect position changes accurately without requiring complex real-time analysis of all sensor parameters, thereby reducing overall system complexity while maintaining detection precision
3Reliability
If multiple sensors with overlapping fields of view are used to detect position changes, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The system merges the functionality of multiple sensors by using their overlapping fields of view to mutually verify position changes. Instead of treating each sensor independently, the system combines their data streams and correlates observations, achieving enhanced detection reliability through the synergistic use of overlapping coverage areas while managing complexity through integrated processing
Data Source
AI summary
A system for detecting sensor position change (DCPC) on movable vehicle components includes two or more sensors on at least one of the movable vehicle components. The sensors detect optical information within a distinct field of view (FOV) about an environment surrounding the vehicle. The distinct FOV of each sensor at least partially overlaps with an FOV of at least one other sensor. The system includes controllers that execute an application for DCPC. The DCPC application acquires overlapping optical information from the sensors, computes a conditional correspondence probability distribution for feature points in the overlapping optical information, computes a normalized joint entropy of the feature points, determines that the sensor is in a position manageable by the DCPC, continuously monitors positions of each of the sensors, selectively dynamically aligns the sensors, and ensures that the sensors are calibrated for vehicle perception tasks including advanced driver assistance system (ADAS) functions.