Multi-Sensor Blindness Detection for Vehicle Sensor Degradation
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Solution Overview
Problem
Existing sensor systems in vehicles are susceptible to degradation due to dirt, damage, or environmental conditions, leading to false positive and false negative detections, which pose safety risks and system availability issues, particularly in highly autonomous driving scenarios.
Innovation Solution
A method for determining sensor degradation status using data from multiple sensor systems with different modalities, incorporating individual and fusion blindness indicators, to ensure reliable detection and minimize false positives and negatives, integrated into the existing data processing architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensor degradation detection is performed using single sensor data, then detection simplicity is maintained, but false positive rates increase reducing system availability
Solution Approach 1:
The patent combines data from multiple sensor systems with different modalities (camera, radar, lidar, ultrasound) to perform degradation detection. By merging information from heterogeneous sensors, the system achieves more reliable degradation status determination while reducing false positives, as each sensor modality provides complementary information about the environmental conditions and sensor performance.
Solution Approach 2:
The degradation detection mechanism is designed to be modality-agnostic and can process data from various sensor types (optical, electromagnetic, acoustic). The system uses universal processing steps including generating determination variables from raw sensor data and comparing environmental parameters across different sensor modalities to identify degradation patterns applicable to any sensor type.
2Measurement precision
If multi-sensor fusion is used for degradation detection, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the degradation detection process into distinct processing steps: generating determination variables from raw sensor data, comparing environmental parameters across sensors, and determining degradation status based on discrepancies. This segmentation allows complex multi-sensor analysis to be broken down into manageable, modular operations that can be implemented efficiently.
Solution Approach 2:
The system performs selective comparison of environmental parameters rather than processing all possible sensor data. By focusing on specific determination variables that are most indicative of degradation (such as object detection consistency, distance measurements, and environmental condition correlations), the system achieves high detection accuracy without the computational burden of exhaustive multi-sensor fusion.
3Reliability
If sensor degradation is not detected, then system availability is maintained, but safety risks increase due to false negative detections
Solution Approach 1:
The patent implements a feedback mechanism where the degradation detection system continuously monitors sensor performance and provides status information to the automated driving system. By comparing environmental parameters across multiple sensors and analyzing determination variables, the system can identify degradation patterns and provide feedback signals to adjust system operations or alert operators, thereby preventing safety incidents while maintaining availability.
Data Source
AI summary
A method for determining a sensor degradation status of a first sensor system includes: providing data of the first sensor system to represent the environment; providing data of a second sensor system to represent the environment; determining an individual blindness indicator for the first sensor system on the basis of sensor data exclusively of the first sensor system; determining at least one first environment-related determination variable based on the provided data of the first sensor system; determining at least one second environment-related determination variable based on the provided data of the second sensor system; determining a fusion blindness indicator based on a comparison of the at least one first environment-related determination variable with the at least one second environment-related determination variable; and determining the sensor degradation status of the first sensor system based on of the individual blindness indicator and the fusion blindness indicator.