Abstract
This work investigates the validity of an occupancy grid mapping inspired by human cognition and the way humans visually perceive the environment. This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver. The mechanisms behind human perception could provide cues on how to improve common techniques employed in autonomous navigation - specifically the use of occupancy grids to represent the environment. We experiment with a neural network that maps an image of the scene onto an occupancy grid representation, and we show how the model benefits from two key (and yet simple) changes: 1) a different format of occupancy grid that resembles the way the brain projects the environment into a warped representation in the cortical visual area; 2) a mechanism similar to human visual attention that filters out non-relevant information from the scene. These effective expedients can potentially be applied to any autonomous driving task requiring an abstract representation of the scenario like the occupancy grids.
Original language | English |
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Title of host publication | Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 |
Publisher | IEEE |
Pages | 2934-2941 |
ISBN (Electronic) | 978-1-6654-0191-3 |
DOIs | |
Publication status | Published - 2021 |
Event | 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada Duration: 11 Oct 2021 → 17 Oct 2021 |
Conference
Conference | 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 11/10/21 → 17/10/21 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.