Sensor Pollution Detection via Cross-Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Sensors used in vehicles, such as camera and LIDAR sensors, can become polluted with dirt and other environmental contaminants, leading to inaccurate information collection and impacting downstream processes like object detection and tracking.
Innovation Solution
A system that utilizes two sensors with overlapping fields of view to detect discrepancies in object detection, determining when one or both sensors need cleaning by comparing outputs from both sensors and sending a signal to the operator if discrepancies are found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensors are used continuously for object detection, then productivity is improved, but sensor pollution accumulates leading to decreased measurement precision
Solution Approach 1:
The system performs preliminary detection by comparing sensor outputs before pollution significantly degrades measurement precision. By continuously monitoring for discrepancies between redundant sensors, the system identifies pollution early and alerts operators to clean sensors before detection accuracy is compromised
Solution Approach 2:
The system implements feedback by continuously comparing outputs from multiple sensors and providing real-time information about sensor health status. When discrepancies indicate pollution, the system feeds back this information to operators, enabling timely maintenance actions to restore measurement precision
2Measurement precision
If redundant sensors are deployed for cross-validation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges the functions of multiple sensors into a unified pollution detection mechanism. By combining outputs from redundant sensors and analyzing discrepancies through a single processing system, the approach achieves improved measurement precision while avoiding the complexity of entirely separate detection and monitoring systems
Solution Approach 2:
The redundant sensors serve multiple functions: primary object detection and pollution self-diagnosis. This multi-functionality allows the system to improve measurement precision through redundancy while avoiding additional dedicated complexity for pollution detection, as the same sensors perform both detection roles
Data Source
AI summary
A system includes a processor and a memory in communication with the processor having one or more modules. The modules include instructions that cause the processor to receive first sensor information from a first sensor and second sensor information of a shared field of view. The instructions cause the processor to detect one or more objects within the shared field of view using the first sensor information and the second sensor information. When one or more discrepancies are identified between the one or more objects detected using the first sensor information and the one or more objects detected using the second sensor information are detected, the instructions that cause the processor to determine that the first sensor or the second sensor needs cleaning.