Sensor Data Processing for Redundant Failure Compensation
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Solution Overview
Problem
Current sensor systems in robotics and autonomous driving face challenges in maintaining availability and safety due to sensor failures from contamination, icing, and other degrading influences, leading to potential risks and functional limitations, as each camera is typically dedicated to specific tasks and lacks redundancy for failure compensation.
Innovation Solution
A method for processing sensor data in systems with multiple sensors that involves reading data in parallel, checking for impairments such as blindness, and adapting the use of sensor data streams to select a main data path, ensuring continued system functionality and increased availability by utilizing redundant data from overlapping detection areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cameras are dedicated to specific tasks without redundancy, then system complexity is reduced, but system availability deteriorates when sensor failures occur
Solution Approach 1:
The patent enables cameras to serve multiple functions dynamically. The failure detection unit identifies when a camera fails, and the system adapts by reallocating remaining functional cameras to cover multiple tasks. This allows a single functional camera to perform both its original task and compensate for the failed camera's task, achieving multi-functionality without adding hardware redundancy.
Solution Approach 2:
The system dynamically adjusts camera assignments based on real-time failure detection. When a camera is detected as failed, the system transitions from static task assignment to dynamic adaptation, where the allocation of camera resources changes according to the current operational status. This dynamic reconfiguration maintains system availability without requiring permanent redundant hardware for each function.
2Reliability
If redundant sensors are added to compensate for failures, then system availability is improved, but device complexity increases
Solution Approach 1:
The system uses its existing camera resources to detect failures in other cameras. The failure detection unit processes images from functional cameras to identify when other cameras have failed (e.g., by detecting blindness or contamination). This self-diagnostic capability eliminates the need for separate redundant sensors dedicated solely to failure detection, as the system services its own monitoring needs using existing resources.
Solution Approach 2:
The same camera network serves dual purposes: normal environmental perception and failure detection. By analyzing images from functional cameras, the system can detect failures in other cameras without requiring dedicated monitoring hardware. This multi-functional use of existing sensors avoids increasing device complexity while maintaining availability.
3Ease of operation
If static camera assignments are used for specific functions, then ease of operation is improved, but adaptability deteriorates when sensor impairments occur
Solution Approach 1:
The system maintains simple static camera assignments during normal operation, preserving ease of operation. When a failure is detected by the failure detection unit, the system dynamically adapts by reconfiguring camera assignments. The failure compensation unit automatically adjusts which cameras perform which functions, allowing the system to adapt to sensor impairments without complex manual reconfiguration, thus maintaining both ease of operation and adaptability.
Solution Approach 2:
The failure detection unit continuously monitors camera status and provides feedback to the control system. When degradation or failure is detected (such as blindness, contamination, or icing), this feedback triggers automatic adaptation of camera assignments. This closed-loop feedback mechanism enables the system to adapt to sensor impairments automatically, maintaining versatility without requiring complex manual intervention.
Data Source
AI summary
A method for processing sensor data in a system that includes multiple sensors for detecting at least a subarea of surroundings around the system. The method includes at least the following steps: a) reading in sensor data detected at least partially in parallel, b) checking whether an at least partial impairment of the detection by the respective sensor may be established for one or for multiple of the sensors on the basis of the read-in sensor data, c) adapting the use of the sensor data, taking the check from step b) into account.