Safe Autonomous Taxiing with Vision-Based Navigation
National Aeronautics and Space Administration · ARC: Ames Research Center
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- Not reported
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Science
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
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.
Expected benefits
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.
System outputs
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.
Technology
- AI classification
- Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
- System name(s)
- Not reported
- Custom-developed code
- Not reported
- Public source code
- Not reported
Sourcing
- How it was built
- Not reported
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- Not reported
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- Not reported
- Demographic features
- Not reported
- Training and evaluation data
- Not reported
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Potential impacts and how they were identified
Not reported