Aircraft Collision Awareness Using Multi-Camera Position Tracking
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Solution Overview
Problem
Wingtip collisions during ground operations pose a significant safety risk and operational challenge due to increased aircraft traffic and complex airframe configurations, with existing collision awareness systems like GNSS often providing inaccurate warnings.
Innovation Solution
A collision awareness system utilizing multiple cameras mounted on a vehicle to determine the position and velocity of objects, generating alerts when the distance between the vehicle and objects is less than a threshold level, thereby predicting potential collisions with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS is used to compute or alert wingtip collisions, then collision awareness is provided, but measurement precision deteriorates with errors of approximately twenty feet or more
Solution Approach 1:
The patent introduces cameras as intermediary devices mounted on the aircraft to capture images of the surrounding environment. These camera images serve as a mediator between the GNSS system and the collision detection function, providing visual confirmation and more precise position data to supplement the imprecise GNSS measurements.
Solution Approach 2:
The patent replaces the purely electronic/GNSS-based collision detection system with a hybrid system that incorporates optical sensing through cameras. This substitution introduces a different sensing modality that is not subject to GNSS error limitations, thereby improving measurement precision while maintaining collision awareness functionality.
2Measurement precision
If multiple cameras are mounted on the vehicle to improve collision detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent makes the camera system multi-functional by using the same cameras for both collision detection and general situational awareness. The processing circuitry analyzes camera images to determine not only collision risks but also to identify and track various objects in the environment, thereby justifying the added complexity through multiple useful functions.
Solution Approach 2:
The system uses the camera images themselves to serve multiple purposes - the same visual data is processed to determine object position, velocity, and collision risk simultaneously. This self-service approach means the additional sensors are paying for themselves by providing multiple functions from a single data source.
3Measurement precision
If costly radar or ultrasound sensors are used to improve collision detection, then measurement precision improves, but loss of substance increases due to high cost
Solution Approach 1:
The patent employs cameras, which are relatively inexpensive compared to radar or ultrasound sensors, as the primary sensing mechanism for collision detection. By using these lower-cost optical sensors instead of expensive electronic sensors, the system achieves acceptable measurement precision while significantly reducing the loss of substance in terms of system cost.
Data Source
AI summary
In some examples, a collision awareness system includes two cameras mounted on portions of a vehicle and processing circuitry configured to determine a position of an object based on an image captured by a first camera when the object is within a field of view of the first camera. The processing circuitry is further configured to determine the position of the object based on an image captured by a second camera when the object is within a field of view of the second camera. The processing circuitry is also configured to determine whether a distance between a future position of the vehicle and a current or future position of the object is less than a threshold level. In response to determine that the distance is less than the threshold level, the processing circuitry can generate an alert.