3D Collision Avoidance Path Screening for Nuisance Alert Reduction
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Solution Overview
Problem
Current Terrain Awareness Warning Systems (TAWS) in aviation often generate excessive false positives, leading to pilot desensitization and reduced situational awareness, as they rely on low-resolution environmental data to determine collision risks, which can result in missed warnings or system disablement.
Innovation Solution
A method that uses three-dimensional object data points and craft state data to determine sets of manoeuvre paths and distance thresholds, identifying colliding paths and providing actionable warnings to avoid collisions, thereby reducing false alarms and enhancing safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TAWS generates a high number of false positive warnings, then collision risks may be detected, but pilot situational awareness is significantly reduced due to desensitization
Solution Approach 1:
The system implements feedback mechanisms that learn from pilot responses to warnings, adjusting warning generation criteria to reduce false positives while maintaining true hazard detection. The feedback loop continuously refines the balance between detection sensitivity and operational usability.
Solution Approach 2:
The warning system dynamically adapts its sensitivity and threshold parameters based on operational context, craft state, and environmental conditions. This dynamic adjustment ensures warnings are generated only when truly necessary, maintaining pilot awareness without causing desensitization.
2Reliability
If TAWS uses crude methods to determine risk of collision, then false negatives are minimized, but the system complexity increases and false positives increase
Solution Approach 1:
The risk determination process is segmented into multiple stages, each handling specific aspects of collision assessment. This segmentation breaks down the complex determination process into manageable components, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system applies partial analysis methods for routine situations and full analysis only when necessary. By using simplified assessment for common scenarios and detailed assessment only when risk indicators are present, the system reduces complexity while maintaining detection coverage.
Data Source
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AI summary
The present disclosure relates to a method for determining an action for collision avoidance in a craft. The method (100) comprises obtaining (110) object data comprising three-dimensional object data points (420); obtaining (120) state data of the craft (260); determining (140) at least one set of manoeuvre paths (410a,b,c) for the craft (260) based on the obtained craft state data; determining (150) a set of distance thresholds (421) for the three-dimensional object data points (420) based on the object data; comparing (160) each set of manoeuvre paths (410a,b,c) with the object data and the set of distance thresholds (421), wherein the set of manoeuvre paths (410a,b,c) is identified as a colliding set of manoeuvre paths (410a,b,c) when each path of the set of manoeuvre paths (410a,b,c) is at least partially within the corresponding distance threshold (421) of at least one three-dimensional object data point (420); and determining (170) an action upon identification of at least one colliding set of manoeuvre paths (410a,b,c).