Vehicle Object Distance Validation Using Sequential Range Gaps
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Solution Overview
Problem
Vehicles' monitoring devices, such as radar sensors, are subjected to disturbances like 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
1Adaptability or versatility
If monitoring devices are used to measure distance in various environmental conditions, then the vehicle can detect surrounding objects and individuals, but the 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 disturbances before they significantly impact operation. By examining differences between consecutive measurements and counting unmeasured distances in advance, the system proactively identifies when noise or interference is affecting the monitoring device, allowing for preventive action or alerting before unreliable measurements compromise vehicle safety operations.
2Reliability
If threshold-based filtering is applied to distinguish noise from legitimate movement, then measurement reliability is improved, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The analysis process is segmented into distinct, simple operations: calculating differences between sequential measurements, counting occurrences of unmeasured distances, and comparing against thresholds. This segmentation breaks down the complex task of noise filtering into manageable, independent steps that can be executed efficiently by processing circuitry, reducing overall system complexity while maintaining high measurement reliability.
Solution Approach 2:
The system uses the monitoring device's own measurement data to identify and filter out noise and interference. By analyzing patterns in the measurements themselves (such as unrealistic differences between sequential readings or gaps in distance coverage), the system enables the monitoring device to self-diagnose and self-correct, eliminating the need for external calibration or complex additional sensing hardware.
3Measurement precision
If the system analyzes multiple sequential data samples to determine reliability, then measurement accuracy is improved, but the response time increases due to additional processing
Solution Approach 1:
The system extracts only the essential features from sequential measurements that indicate disturbance patterns: the difference between consecutive distance values and the count of unmeasured distances within a time window. By extracting and analyzing only these critical parameters rather than processing complete raw measurement datasets, the system achieves high measurement precision while minimizing processing time and computational overhead.
Data Source
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AI summary
In an aspect, A computer system (14) of a vehicle (10) configured to monitor vehicle surroundings is provided. The computer system comprises processing circuitry (11) configured to acquire (S101) data samples from a monitoring device (15) configured to measure a distance to an object (20, 21) located within a field of view (17) of the monitoring device (15), determine (S102) a difference between at least two sequential data samples of the measured distance from the monitoring device (15) to the object (20, 21), and if said difference exceeds (S103) a set first threshold value (TD), determine (S104) a number of distances within an operating range (d) of the monitoring device (15) to the object (20, 21) that are not measured by the monitoring device (15) during a set time period, wherein the measured distance to the object (20, 21) is not relied upon if the number of distances that are not measured during the set time period is below (S105) a set second threshold value (Tz).