Generating medium-scale synthetic snowy scenes for testing autonomous vehicle navigation

Christopher Goodin; Daniel W. Carruth; Lalitha Dabbiru; Lucas D. Cagle; Nicholas Harvel; John G. Monroe; Michael W. Parker

doi:10.1117/12.3009866

7 June 2024 Generating medium-scale synthetic snowy scenes for testing autonomous vehicle navigation

Christopher Goodin, Daniel W. Carruth, Lalitha Dabbiru, Lucas D. Cagle, Nicholas Harvel, John G. Monroe, Michael W. Parker

Author Affiliations +

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 130350J (2024) https://doi.org/10.1117/12.3009866
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The accumulation of falling snow is a complex physical process that involves a variety of environmental factors. While much past work has been done on the rendering of accumulated snow for gaming applications, scientific simulation of snow accumulation has been limited to large-scale mountain ranges and watersheds. These largescale simulations are not relevant for simulations of autonomous ground vehicle (AGV) performance, for which the relevant length scales are a few meters to a few hundred meters. In this work, we present a physics-based simulation of the accumulation of falling snow that is implemented using smoothed-particle hydrodynamics (SPH) to represent snow mass elements. SPH has been used in past work to simulate not only fluids but also deformable and continuous media ranging from concrete to fabric to soil. In this work we show that SPH can be parametrized to have material properties that reasonably approximate the bulk properties of accumulated snow. We present several example simulations in which SPH has been used to calculate the accumulation of fallen snow in an off-road scene. Finally, we show how the SPH simulation output can be combined with a rendering simulation to create realistic synthetic images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Christopher Goodin, Daniel W. Carruth, Lalitha Dabbiru, Lucas D. Cagle, Nicholas Harvel, John G. Monroe, and Michael W. Parker "Generating medium-scale synthetic snowy scenes for testing autonomous vehicle navigation", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130350J (7 June 2024); https://doi.org/10.1117/12.3009866

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