DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Scenario-Based Synthetic Data Generation for an AI-based System Using a Flight Simulator

Sprockhoff, Jasper and Gupta, Siddhartha and Durak, Umut and Krüger, Thomas (2024) Scenario-Based Synthetic Data Generation for an AI-based System Using a Flight Simulator. AIAA SCITECH 2024 Forum, 2024-01-08 - 2024-01-12, Orlando, USA. doi: 10.2514/6.2024-1462.

[img] PDF - Only accessible within DLR

Official URL: https://arc.aiaa.org/doi/10.2514/6.2024-1462


In recent years, algorithms based on machine learning have significantly advanced many technical areas, including computer vision. Since the performance of machine learning applications is data-dependent, a sufficient amount of high-quality data must be available to achieve robust and stable performance. However, the collection of large amounts of real-world data that covers the operational parameters of the AI-based system is often a difficult task because of availability, cost, or even potential danger. Therefore, synthetic data generation is often used to supplement data sets with additional required data samples. In this paper, we propose a baseline for an automated toolchain to generate synthetic image data of aircraft for machine-learning computer vision applications using a flight simulator. Scenario-based approaches have shown applicability to systematically generate valid test cases for system safety evaluation. We leverage a similar approach to generate data for training of AI-based systems. Our approach requires the user to create scenario models using our modelling tool. These models define the operational ranges for a set of parameters that characterize executable scenarios. The scenarios defined by the models are used to automatically produce images from simulations carried out with the FlightGear open-source flight simulator. We distinguish between a static and a dynamic simulation approach. The static approach generates a sequence of independent scenes, while the dynamic approach creates situations that mimic a collision avoidance scenario. With our approach, we can automatically generate large amounts of raw image data covering the relevant parameter ranges based on the models created by the user.

Item URL in elib:https://elib.dlr.de/202056/
Document Type:Conference or Workshop Item (Speech)
Title:Scenario-Based Synthetic Data Generation for an AI-based System Using a Flight Simulator
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sprockhoff, JasperUNSPECIFIEDhttps://orcid.org/0009-0005-5725-0726150911876
Date:4 January 2024
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Artificial Intelligence, Machine Learning, Synthetic Data, Computer Vision, Collision Avoidance, Scenario Modelling, Scenarios, Operational Design Domain, ODD, Flight Simulator, FlightGear
Event Title:AIAA SCITECH 2024 Forum
Event Location:Orlando, USA
Event Type:international Conference
Event Start Date:8 January 2024
Event End Date:12 January 2024
Organizer:American Institute of Aeronautics and Astronautics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Aircraft Systems
Location: Braunschweig
Institutes and Institutions:Institute of Flight Systems > Safety Critical Systems&Systems Engineering
Institute for AI Safety and Security
Deposited By: Sprockhoff, Jasper
Deposited On:17 Jan 2024 11:21
Last Modified:24 Apr 2024 21:02

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.