Fidelity requirements for simulating sensor performance in autonomous ground vehicles

Chris Goodin; Daniel W. Carruth; Lalitha Dabbiru; Michael Hedrick; Zachary S. Aspin; Justin T. Carrillo; John Kaniarz

doi:10.1117/12.2661663

13 June 2023 Fidelity requirements for simulating sensor performance in autonomous ground vehicles

Chris Goodin, Daniel W. Carruth, Lalitha Dabbiru, Michael Hedrick, Zachary S. Aspin, Justin T. Carrillo, John Kaniarz

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 1252908 (2023) https://doi.org/10.1117/12.2661663
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Simulation has become an important enabler in the development and testing of autonomous ground vehicles (AGV), with simulation being used both to generate training data for AI/ML-based segmentation and classification algorithms and to enable in-the-loop testing of the AGV systems that use those algorithms. Furthermore, digital twins of physical test areas provide a safe, repeatable way to conduct critical safety and performance testing of these AI/ML algorithms and their performance on AGV systems. For both these digital twins and the sensor models that use them to generate synthetic data, it is important to understand the relationship between the fidelity of the scene/model and the accuracy of the resulting synthetic sensor data. This work presents a quantitative evaluation of the relationship between digital scene fidelity, sensor model fidelity, and the quality of the resulting synthetic sensor data, with a focus on camera data typically used on AGV to enable autonomous navigation.

Conference Presentation

Citation Download Citation

Chris Goodin, Daniel W. Carruth, Lalitha Dabbiru, Michael Hedrick, Zachary S. Aspin, Justin T. Carrillo, and John Kaniarz "Fidelity requirements for simulating sensor performance in autonomous ground vehicles", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 1252908 (13 June 2023); https://doi.org/10.1117/12.2661663

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