Radar Tracking Slow Moving Objects Classification
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Solution Overview
Problem
Radar sensors face challenges in accurately classifying slow-moving objects, such as pedestrians or cars, due to ambiguous detections at lower speeds, leading to potential misclassification as stationary objects.
Innovation Solution
A system that uses a combination of radar detectors, Kalman filters, and Low Pass filters to determine the velocity and movement of objects, classifying them as slow-moving or stationary based on specific criteria such as distance, speed, and variance in range rate, to improve accuracy in object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radar sensors are used to detect objects at lower speeds, then detection coverage is improved, but measurement precision deteriorates due to ambiguous detections making objects difficult to classify as moving or stationary
Solution Approach 1:
The patent introduces an intermediary classification system that mediates between radar detection and object classification. This system uses multiple parameters (range rate variance, centroid movement, distance traveled) as intermediate indicators to resolve ambiguous detections, transforming the direct classification problem into a multi-stage evaluation process that improves accuracy for slow-moving objects
Solution Approach 2:
The patent changes the classification parameters from simple velocity thresholding to a multi-parameter evaluation system. By incorporating range rate variance, centroid displacement, and distance traveled over time, the system adapts the classification criteria to better suit slow-moving objects, resolving the measurement precision issue while maintaining detection coverage
2Device complexity
If simple velocity thresholding is used for object classification, then device complexity is reduced, but reliability deteriorates due to misclassification of slow moving objects as stationary
Solution Approach 1:
The patent segments the classification process into distinct stages: initial velocity threshold screening, followed by detailed evaluation of range rate variance, centroid movement, and distance traveled. This segmentation allows the system to maintain simplicity for fast-moving objects while applying more reliable multi-parameter analysis only when needed for slow-moving or ambiguous cases, thus improving reliability without excessive complexity
Solution Approach 2:
The patent applies preliminary velocity threshold filtering before conducting more complex analysis. This preliminary action quickly eliminates obvious cases, allowing the system to focus computational resources on ambiguous slow-moving objects where reliability is most critical, thereby balancing device complexity and classification reliability
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 of object classification, reducing misclassification errors and improving the ability of autonomous vehicles to identify and track slow-moving objects effectively.
Implementation Method 1
radar tracking devices provide information regarding objects in a vicinity or pathway of a vehicle
Data Source
AI summary
An illustrative example method of classifying a detected object includes detecting an object, determining that an estimated velocity of the object is below a preselected threshold velocity requiring classification, determining a time during which the object has been detected, determining a first distance the object moves during the time determining a speed of the object from the first distance and the time, determining a second distance that a centroid of the detected object moves during the time, and classifying the detected object as a slow moving object or a stationary object based on a relationship between the first and second distances and a relationship between the estimated velocity and the speed.