Vehicle Sensor Degradation Monitoring Using ROI Data Metrics
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Solution Overview
Problem
Vehicle sensors, such as image and lidar sensors, can experience degraded performance due to environmental factors and internal errors, leading to suboptimal data quality that impacts navigation and obstacle detection in autonomous vehicles.
Innovation Solution
A method to determine a degraded state of sensors by evaluating data metrics, such as contrast ratios and lidar point intensity, within a region of interest, allowing for actions like sensor cleaning or trajectory adjustments to be taken based on the sensor's condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to capture sensor data for object detection, then navigation and obstacle detection capabilities are improved, but sensor degradation from environmental factors and internal errors reduces data quality and reliability
Solution Approach 1:
The system performs preliminary evaluation of sensor data quality by computing data metrics (such as contrast ratios for image sensors or intensity metrics for LiDAR) before using the sensor data for navigation and obstacle detection. This allows the autonomous vehicle to proactively identify degraded sensor data and take corrective actions such as switching to backup sensors, adjusting sensor parameters, or alerting operators before degradation critically impacts safety
Solution Approach 2:
The system establishes a feedback loop where sensor data quality is continuously monitored through computed data metrics, and this quality information feeds back into the sensor selection and data processing pipeline. When degradation is detected, the system can adjust its sensor usage strategy in real-time, such as switching between redundant sensors or adjusting exposure parameters, thereby maintaining reliable operation despite environmental harmful factors
2Extent of automation
If sensor data is used for autonomous vehicle navigation, then vehicle autonomy is improved, but degraded sensor data increases the risk of navigation errors and safety issues
Solution Approach 1:
Before executing autonomous navigation based on sensor data, the system preliminarily evaluates the quality of the sensor data by computing data metrics. If the metrics indicate degraded quality (such as low contrast in images or poor intensity distribution in LiDAR data), the system takes preliminary corrective actions such as switching to alternative sensors, adjusting sensor parameters, or requesting human intervention before navigation decisions are made, thereby preventing navigation errors from degraded data
Solution Approach 2:
The system prepares backup sensor data sources and alternative navigation strategies in advance to cushion against potential sensor degradation. When degradation is detected through data metric evaluation, these pre-prepared alternatives are immediately activated to maintain navigation safety, providing a buffer that prevents degraded sensor data from directly compromising autonomous navigation reliability
Data Source
AI summary
Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.