Autonomous Driving Sensor Risk Distribution via MTBF Analysis
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Solution Overview
Problem
Current autonomous driving systems lack a method to quantitatively estimate risks due to performance limitations when deployed in geofenced operational design domains, despite having redundancy and diversification in sensors and hardware.
Innovation Solution
A safety redundancy autonomous driving system that determines risk distribution by obtaining mean time between failure (MTBF) data for each sensor coverage zone, computing performance risks, and updating risk distributions in real-time, distinguishing between permanent and temporary performance limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundancy and diversification are implemented in sensors and hardware, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the autonomous driving system into multiple independent sensor coverage zones, each with its own risk assessment. By segmenting the monitoring space into discrete zones with defined boundaries, the system can evaluate risks independently in each zone while maintaining overall system reliability through comprehensive coverage.
Solution Approach 2:
The system performs preliminary risk assessment by calculating MTBF (Mean Time Between Failures) for each sensor coverage zone before actual failures occur. This proactive approach allows the system to identify potential single-point failures in advance and take preventive measures, thereby improving reliability without requiring complex real-time intervention mechanisms.
2Reliability
If quantitative risk estimation is implemented for sensor performance limitations, then safety management is improved, but measurement precision requirements increase
Solution Approach 1:
The patent introduces MTBF (Mean Time Between Failures) as an intermediary metric to quantify sensor performance limitations and risks. Instead of directly measuring complex performance degradation, the system uses MTBF as a standardized intermediate parameter that bridges sensor hardware characteristics and overall system safety assessment, making risk estimation more manageable and precise.
Solution Approach 2:
The system transforms qualitative sensor performance assessments into quantitative risk values by changing the parameter representation from general performance metrics to specific MTBF-based risk probabilities. This parameter transformation enables precise mathematical calculation and comparison of risks across different sensor zones and failure modes.
3Adaptability or versatility
If real-time risk distribution updates are performed, then system adaptability is improved, but loss of time for computation increases
Solution Approach 1:
The system performs preliminary calculations of MTBF values for each sensor coverage zone based on historical data and sensor specifications before real-time operation. This pre-computation of baseline risk parameters reduces the computational burden during real-time updates, allowing the system to maintain high adaptability by only updating the specific risk distribution values that change with current sensor status rather than recalculating everything from scratch.
Data Source
AI summary
Systems and methods of determining a risk distribution associated with a multiplicity of coverage zones covered by a multiplicity of sensors of an autonomous driving vehicle (ADV) are disclosed. The method includes for each coverage zone covered by at least one sensor of the ADV, obtaining MTBF data of the sensor(s) covering the coverage zone. The method further includes determining a mean time between failure (MTBF) of the coverage zone based on the MTBF data of the sensor(s). The method further includes computing a performance risk associated with the coverage zone based on the determined MTBF of the coverage zone. The method further includes determining a risk distribution based on the computed performance risks associated with the multiplicity of coverage zones.