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LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems

Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe, Augustin Fuchs, Jean-Brice Ginestet, Jacques Girard, Sofiane Kraiem, Franck Mamalet, Vincent Mussot, Claire Pagetti, Thierry Sammour

Abstract

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems

Abstract

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

Paper Structure

This paper contains 23 sections, 2 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Operational Design Domain
  3. Approach cone
  4. Other hypotheses
  5. Intended function
  6. Synthetic data generators
  7. Interoperability
  8. Calibration
  9. Dataset - LARD v2
  10. Strategy of dataset definition
  11. Labelling
  12. ML tasks and metrics
  13. ML tasks
  14. Metrics - extended mAP
  15. Experiments
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Distribution of runways per airports in LARD v2
  • Figure 2: VBL constituent architecture with 3 stages
  • Figure 3: Lard V2 scenario generation and labeling workflow.
  • Figure 4: Example of .yaml scenario file used in LARD v2, showing an aircraft pose at Toulouse Blagnac airport (LFBO), during an approach on runway 14R.
  • Figure 5: Notations to define mAP and extended mAP
  • ...and 2 more figures