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<h1 class="title is-1 publication-title">DriViDOC: Driving from Vision through Differentiable Optimal Control</h1>
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<a href="https://scholar.google.com/citations?user=cGgoHBUAAAAJ&hl">Flavia Sofia Acerbo</a><sup>1,2,3</sup>,</span>
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<a href="https://www.kuleuven.be/wieiswie/en/person/00015548">Jan Swevers</a><sup>2</sup>,</span>
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<a href="https://www.esat.kuleuven.be/psi/TT">Tinne Tuytelaars</a><sup>3</sup>,
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<a href="https://tongduyson.github.io/">Son Tong</a><sup>1</sup>,
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<span class="author-block"><sup>1</sup>Siemens Digital Industries Software, Leuven, Belgium</span>
<span class="author-block"><sup>2</sup>Dept. of Mechanical Engineering, KU Leuven, Belgium</span>
<span class="author-block"><sup>2</sup>Dept. of Electrical Engineering (ESAT), KU Leuven, Belgium</span>
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<span class="dnerf" style="color: blue; font-size: 24px; font-weight: bold;">Accepted to IROS 2024!</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations.
DriViDOC combines the automatic inference of relevant features from camera frames with the properties of nonlinear model predictive control (NMPC), such as constraint satisfaction.
Our approach leverages the differentiability of parametric NMPC, allowing for end-to-end learning of the driving model from images to control. The model is trained on an offline dataset comprising various human demonstrations collected on a motion-base driving simulator. During online testing, the model demonstrates successful imitation of different driving styles, and the interpreted NMPC parameters provide insights into the achievement of specific driving behaviors. Our experimental results show that DriViDOC outperforms other methods involving NMPC and neural networks, exhibiting an average improvement of 20% in imitation scores.
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As a byproduct of our method, we can also solve the matting problem by ignoring
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We can also animate the scene by interpolating the deformation latent codes of two input
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There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
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<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
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Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
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There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
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<pre><code>@inproceedings{acerbo2024drividoc,
author = {Acerbo, Flavia Sofia and Swevers, Jan and Tuytelaars, Tinne and Tong, Son},
title = {Driving from Vision through Differentiable Optimal Control},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024},
}</code></pre>
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