Safe Autonomous Taxiing with Vision-Based Navigation
Description
TaxiNet is a vision-based deep learning model developed to enable autonomous vehicles to follow a designated line safely during aircraft taxiing, a crucial application for assured autonomy research. This project’s goal is to demonstrate safe, closed-loop control using neural network perception from camera images, focusing on meeting rigorous safety standards for learning-enabled components (LECs) in safety-critical contexts.
Detailed example
We developed both a physical rover for real-world testing and a simulator in Unreal Engine with a realistic NASA Ames campus model for extensive, simulation-based validation.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
TaxiNet is a vision-based deep learning model developed to enable autonomous vehicles to follow a designated line safely during aircraft taxiing, a crucial application for assured autonomy research. This project’s goal is to demonstrate safe, closed-loop control using neural network perception from camera images, focusing on meeting rigorous safety standards for learning-enabled components (LECs) in safety-critical contexts.
Controls / human review
ATO: Not reported; PIA: Not published