Self-Supervised Learning of Camera-based Drivable Surface Roughness

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354062" target="_blank" >RIV/68407700:21230/21:00354062 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IV48863.2021.9575288" target="_blank" >https://doi.org/10.1109/IV48863.2021.9575288</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IV48863.2021.9575288" target="_blank" >10.1109/IV48863.2021.9575288</a>

Result language
angličtina
Original language name
Self-Supervised Learning of Camera-based Drivable Surface Roughness
Original language description
A self-supervised method to train a visual predictor of drivable surface roughness in front of a vehicle is proposed. A convolutional neural network taking a single camera image is trained on a dataset labeled automatically by a cross-modal supervision. The dataset is collected by driving a vehicle on various surfaces, while synchronously recording images and accelerometer data. The surface images are labeled by the local roughness measured using the accelerometer signal aligned in time. Our experiments show that the proposed training scheme results in accurate visual predictor. The correlation coefficient between the visually predicted roughness and the true roughness (measured by the accelerometer) is 0.9 on our independent test set of about 1000 images. The proposed method clearly outperforms a baseline method which has the correlation of 0.3 only. The baseline is based on surface texture strength without any training. Moreover, we show a coarse map of local surface roughness, which is implemented by scanning an input image with the trained convolutional network. The proposed method provides automatic and objective road condition assessment, enabling a cheap and reliable alternative to manual data annotation, which is infeasible in a large scale.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 2021 IEEE Intelligent Vehicles Symposium (IV)
ISBN
978-1-7281-5394-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1319-1325
Publisher name
IEEE
Place of publication
Piscataway
Event location
Nagoya
Event date
Jul 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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