Autonomous Vehicle Sensor Reliability Management
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and managing minor damage to their sensor networks, which can lead to unreliable sensor states and disable the vehicle, despite remaining in a limited but reliable functional state, due to the complexity and sensitivity of the Advanced Driver Assistant System (ADAS) components.
Innovation Solution
A post-incident management system that utilizes a controller with processing circuitry to collect and analyze data from multiple sensors, generate reliability indices, and create a virtual sensor when a failure is detected, allowing the vehicle to continue operations by forming a platoon with other vehicles to compensate for failed sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses robust and calibrated sensor networks with redundant collections of complementary sensor modalities, then the reliability of sensor detection is improved, but the complexity of the system increases making it difficult to detect minor damage
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor health metrics and generating damage assessments before minor damage progresses to complete failure. The controller evaluates sensor data quality, calibration status, and operational parameters in advance to detect subtle changes indicating minor damage, allowing preventive maintenance or compensation strategies to be implemented before the sensor becomes completely non-functional.
2Productivity
If the vehicle operates with minimal or non-apparent damage to sensor networks, then the vehicle can remain in a limited but reliable functional state, but the damage is difficult to detect
Solution Approach 1:
The system implements continuous feedback mechanisms by monitoring sensor output quality, signal consistency, and calibration parameters. The controller compares actual sensor readings against expected values and detects deviations indicating minor damage. This feedback loop enables the system to identify subtle changes in sensor performance and adjust operations or trigger maintenance protocols before the damage becomes apparent or causes complete sensor failure.
3Measurement precision
If the sensor network is highly sensitive to detect minor damage, then the detection capability is improved, but the system becomes more susceptible to false alarms and operational disruptions
Solution Approach 1:
The system merges multiple sensor modalities and evaluation criteria to assess sensor health comprehensively. Instead of relying on a single sensitive threshold, the controller integrates data from complementary sensors, analyzes multiple parameters (signal quality, calibration status, operational consistency), and uses ensemble evaluation methods. This combination approach maintains high detection precision while reducing false alarms through cross-validation and consensus among multiple measurement sources.
Data Source
AI summary
In one example a management system for an autonomous vehicle, comprises a first image sensor to collect first image data in a first geographic region proximate the autonomous vehicle and a second image sensor to collect second image data in a second geographic region proximate the first geographic region and a controller communicatively coupled to the first image sensor and the second image sensor and comprising processing circuitry to collect the first image data from the first image sensor and second image data from the second image sensor, generate a first reliability index for the first image sensor and a second reliability index for the second image sensor, and determine a correlation between the first image data and the second image data. Other examples may be described.