Multipath Interference Detection in Vehicle Time-of-Flight Sensors
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Solution Overview
Problem
Existing vehicle sensors face challenges in accurately interpreting environments with multiple surfaces at different distances due to multipath interference, leading to unreliable data and decreased efficiency in identifying potential obstacles.
Innovation Solution
Techniques for detecting and mitigating multipath interference in sensor data, including time-of-flight sensors, involve determining anomalous data through comparisons of depth and intensity values, applying region growing and edge detection operations, and using threshold values to identify and correct or smooth out images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-of-flight sensors are used to capture sensor data in environments with multiple surfaces at different distances, then the vehicle can detect surfaces, but multipath interference causes ambiguous sensor returns and unreliable data
Solution Approach 1:
The patent uses intensity data as an intermediary to detect and mitigate multipath interference. By comparing intensity values with depth data, the system identifies anomalies caused by multipath effects and corrects the depth measurements accordingly, resolving the reliability issue while maintaining measurement precision
Solution Approach 2:
The system implements feedback by using detected multipath interference patterns to adjust and correct sensor data processing. The identified anomalies feed back into the depth calculation process to compensate for multipath effects, improving both reliability and accuracy of surface detection
2Productivity
If traditional sensor data processing is used without multipath interference detection, then processing is simpler, but processing time increases and efficiency decreases in identifying potential obstacles
Solution Approach 1:
The patent applies preliminary action by performing region growing and edge detection operations before final obstacle identification. This preprocessing steps organize the sensor data in advance, making the subsequent obstacle detection more efficient and reducing overall processing time despite the added initial processing steps
Solution Approach 2:
The system segments the sensor data processing into distinct stages: region growing to identify potential surfaces, edge detection to refine boundaries, and final obstacle identification. This segmentation allows each stage to be optimized independently, improving overall productivity while managing processing time effectively
3Ease of operation
If sensor data from environments with multiple surfaces is processed without specialized techniques, then the system is simpler to operate, but the data becomes ambiguous and difficult to interpret
Solution Approach 1:
The patent changes parameters by comparing multiple data types (depth and intensity) and using threshold-based anomaly detection. This approach maintains ease of operation through automated processing while preventing information loss by systematically analyzing multiple sensor parameters to resolve ambiguities in complex environments
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and efficiency of surface detection, reducing processing time and improving safety by minimizing errors in obstacle identification and navigation.
Implementation Method 1
A vehicle may use time-of-flight sensors to capture sensor data to detect surfaces in an environment
Data Source
AI summary
Techniques for determining multipath interference associated with a surface in an environment based on sensor data are discussed herein. The sensor data may be captured and received from a sensor associated with a vehicle travelling through an environment. The sensor data may include depth data (which may be received as phase data) and intensity data associated with the environment. The depth data and the intensity data may be associated with various surfaces in the environment. Based on the relative changes between corresponding depth data and intensity data for a particular surface, multipath interference associated with either of the depth data or the intensity data may be determined.