Vehicle Distance Sensing Reliability Under Noise and Interference
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Solution Overview
Problem
Vehicles' monitoring devices, such as radar sensors, are prone to disturbances from noise, interference, and severe weather conditions, leading to unreliable distance measurements.
Innovation Solution
A computer system in vehicles determines the reliability of distance measurements by analyzing differences between sequential data samples and the number of unmeasured distances within a set time period, using threshold values to distinguish between noise-induced errors and legitimate object movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If monitoring devices are used to measure distance to objects, then vehicle surroundings can be monitored, but measurement reliability deteriorates due to noise, interference, and severe weather conditions
Solution Approach 1:
The system performs preliminary analysis of sequential distance measurements to detect patterns indicating noise or interference before making a final reliability determination. By examining multiple sequential samples and calculating differences between them, the system proactively identifies measurement issues before they affect vehicle control decisions.
Solution Approach 2:
The system continuously monitors measurement quality by comparing sequential distance samples and provides feedback on measurement reliability. This feedback mechanism allows the system to adjust its trust in measurements based on detected patterns of noise or interference, improving overall measurement reliability in adverse conditions.
2Reliability
If threshold values are used to filter measurements, then measurement reliability improves, but false rejection of legitimate measurements increases
Solution Approach 1:
The system uses dynamic thresholding by comparing differences between sequential measurements rather than applying a fixed threshold. This dynamic approach adapts to changing conditions and object motion, reducing false rejection of legitimate measurements while maintaining reliability filtering.
Solution Approach 2:
The system changes the parameter being analyzed from absolute distance values to differences between sequential measurements. This parameter transformation allows the system to detect noise patterns while preserving legitimate measurements that show consistent change patterns, reducing false rejections.
3Reliability
If multiple sequential data samples are analyzed, then measurement reliability improves, but processing time increases
Solution Approach 1:
The system analyzes only the necessary number of sequential samples required to detect noise patterns, rather than processing all available data. By calculating differences between a limited set of sequential measurements, the system achieves sufficient reliability assessment with minimal processing time.
Solution Approach 2:
The system segments the analysis into discrete difference calculations between sequential samples, allowing for efficient processing. This segmented approach breaks down the complex task of reliability assessment into simple, fast-computing difference operations that can be performed in real-time.
Data Source
AI summary
A computer system of a vehicle configured to monitor vehicle surroundings is provided. The computer system comprises processing circuitry configured to acquire data samples from a monitoring device configured to measure a distance to an object located within a field of view of the monitoring device, determine a difference between at least two sequential data samples of the measured distance from the monitoring device to the object, and if said difference exceeds a set first threshold value, determine a number of distances within an operating range of the monitoring device to the object that are not measured by the monitoring device during a set time period. The measured distance to the object is not relied upon if the number of distances that are not measured during the set time period is below a set second threshold value.