Aircraft Collision Detection Using Path Prediction
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Solution Overview
Problem
Current aircraft ground collision avoidance systems generate excessive false alerts, leading to pilot fatigue and increased collision risks due to reliance on radar and camera sensors that cannot accurately predict aircraft paths without nose wheel steering angle data, making them costly and complex to integrate with existing aircraft systems.
Innovation Solution
A collision detection system using an object detection device and processor to predict the aircraft's path based on ground speed and heading, comparing it with detected object locations to provide accurate collision alerts, thereby reducing false positives and integrating with standard ARINC 429 data without requiring additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar and camera sensors are used to detect objects near wingtips, then object detection capability is improved, but the system generates excessive false alerts leading to pilot fatigue
Solution Approach 1:
The system introduces an intermediary path prediction module that uses ground speed and heading data to predict the aircraft's future path. This intermediary calculation acts as a filter between object detection and alert generation, determining whether detected objects actually pose a collision risk based on whether they intersect with the predicted path, thereby reducing false alerts while maintaining detection capability
Solution Approach 2:
The system performs preliminary path prediction before generating collision alerts. By calculating the predicted aircraft path in advance using available ground speed and heading information, the system can pre-assess which detected objects represent genuine collision threats versus false alarms, improving alert reliability before presenting information to the pilot
2Measurement precision
If additional sensors are added to supplement standard aircraft information, then path prediction accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The system achieves path prediction using existing multi-functional aircraft data (ground speed and heading) that serve multiple purposes in flight operations. By leveraging data already collected for navigation and control purposes, the system avoids adding dedicated sensors while maintaining path prediction capability, thus reducing system complexity and cost
Solution Approach 2:
The system uses the aircraft's own existing navigation data (ground speed and heading from ARINC 429) to predict its own path without requiring external sensors. The aircraft essentially serves itself by repurposing data already available in its own systems, eliminating the need for additional sensing equipment
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
The system effectively reduces pilot fatigue by providing reliable collision alerts, minimizing false alarms, and allowing for easy retrofitting onto existing aircraft without complex integration, enhancing safety in congested airport environments.
Implementation Method 1
The object detection device is preferably a radar, although ultrasound or other reflection-type object detection systems may be used instead.
Implementation Method 2
The object detection device is preferably a radar, although ultrasound or other reflection-type object detection systems may be used instead.
Data Source
AI summary
An aircraft ground collision detection system comprising: an object detection device for mounting on an aircraft and arranged to detect objects and output the location of each detected object; and a processor arranged to: receive the ground speed of the aircraft and the heading of the aircraft and the detected location of each detected object; predict the aircraft's path based on the ground speed and the heading; compare the predicted aircraft path with the object locations; and output an alert based on the overlap or proximity of the predicted aircraft path with the object locations. By predicting the path of the aircraft based on detected ground speed and heading, the system can accurately assess which detected objects pose a collision threat.