Foliage Detection Using Range Data Tracking Parameters
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Solution Overview
Problem
Current range-based sensors in autonomous vehicles often incorrectly detect foliage as solid objects, leading to false alarms and unnecessary alerts, which can be inconvenient and unsafe.
Innovation Solution
A system comprising a tracking component, tracking parameters component, and classification component that distinguishes foliage from solid objects by analyzing tracking age, consistency, and variability in range data, allowing for more accurate object classification and decision-making in driving maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If range-based sensors detect all objects in the environment, then detection coverage is improved, but false detection of foliage as solid objects increases
Solution Approach 1:
The system performs preliminary tracking and analysis of detection patterns before final classification. By monitoring objects over multiple frames and establishing tracking parameters (age, consistency, variability) before making a solid object determination, the system preliminarily filters out foliage that exhibits inconsistent tracking patterns, thereby reducing false alarms while maintaining detection coverage
Solution Approach 2:
The system uses feedback from continuous tracking data to refine object classification. Tracking parameters such as detection consistency and position variability are continuously updated based on successive sensor readings, and this feedback loop allows the system to adjust its classification decisions, improving accuracy while reducing false positive detections of foliage as solid objects
2Reliability
If the system classifies all detected features as solid objects, then safety is improved, but user convenience deteriorates due to unnecessary alerts
Solution Approach 1:
The system applies different classification criteria and tracking parameter thresholds to different types of detected features based on their local characteristics. By analyzing specific tracking parameters (age, consistency, variability) for each detected object and applying appropriate classification rules locally, the system can identify foliage with distinct patterns and classify them differently from actual solid objects, thereby maintaining safety while reducing unnecessary alerts that degrade user convenience
3Measurement precision
If tracking parameters are analyzed for every detected feature, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs partial analysis by focusing tracking parameter analysis only on features that meet certain preliminary criteria or exhibit ambiguous characteristics. Rather than analyzing every single detected feature in full detail, the system applies simplified tracking parameter evaluation to obvious cases and more detailed analysis only when needed, thereby maintaining classification accuracy for critical decisions while reducing overall computational complexity
Data Source
AI summary
A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.