Radar Doppler Analysis for Concealed Object Detection Near Targets
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Solution Overview
Problem
Radar systems in autonomous vehicles face challenges with false positive and false negative detections due to radar noise, which can lead to safety risks by concealing smaller, less reflective objects and increasing the likelihood of collisions.
Innovation Solution
Determine object-specific radar response thresholds based on RCS and doppler distributions to identify drivable and non-drivable surfaces, using a radar noise estimator to analyze radar data and adjust thresholds dynamically for improved object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a detection threshold is used to suppress false positive detections, then false positive detections are reduced, but real returns may be suppressed as well
Solution Approach 1:
The patent segments the detection space by creating multiple detection thresholds: a first detection threshold for initial object detection and a second, lower detection threshold for identifying concealed objects. This segmentation allows the system to differentiate between potential false positives and genuine concealed objects by applying different threshold levels at different stages of analysis.
Solution Approach 2:
The patent applies partial action by using a two-stage detection approach where not all regions are subjected to the same strict threshold. Instead, a second detection pass with a lower threshold is applied specifically to regions identified as potentially containing concealed objects, thereby reducing false positives in general areas while maintaining sensitivity in critical regions.
2Measurement precision
If radar noise levels are reduced to improve signal clarity, then false positive detections decrease, but the ability to detect low-RCS objects deteriorates
Solution Approach 1:
The patent implements dynamic threshold adjustment by adapting the detection threshold based on the local radar noise environment. The system calculates noise levels in different regions and dynamically sets detection thresholds relative to these local noise floors, allowing it to maintain signal clarity in high-noise areas while preserving detection capability for low-RCS objects in low-noise areas.
Solution Approach 2:
The patent changes the detection parameter (threshold level) based on environmental conditions. By modifying the detection threshold parameter according to measured radar noise levels and object characteristics, the system optimizes the balance between signal clarity and concealed object detection capability across different operating conditions.
3Measurement precision
If object type-specific thresholds are applied to improve detection accuracy, then measurement precision increases, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing object type classifications and associated threshold parameters before actual detection occurs. The system uses preliminary object characterization (such as expected RCS ranges for different object types) to select appropriate detection thresholds, thereby achieving high detection accuracy without requiring complex real-time threshold calculation during the detection process.
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 safety and efficiency of autonomous vehicle operation by reducing false negatives and positives, allowing accurate detection of concealed objects and optimizing navigation paths.
Implementation Method 1
Radar generally measures the distance from a radar device to the surface of an object by transmitting a radio wave and receiving a reflection of the radio wave from the surface of the object
Implementation Method 2
Radar and doppler analysis and concealed object detection
Data Source
AI summary
Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.