Lane following learning based on semantic segmentation with chroma key and image superposition
| Autor: | Javier Corrochano, Juan M. Alonso-Weber, María Paz Sesmero, Araceli Sanchis |
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| Přispěvatelé: | Comunidad de Madrid, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia e Innovación (España) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Informática
imitation learning TK7800-8360 Computer Networks and Communications feature extraction Imitation learning lane following Semantic segmentation semantic segmentation chroma key automated driving noise addition Lane following Noise addition Automated driving Hardware and Architecture Control and Systems Engineering Signal Processing Feature extraction Electrical and Electronic Engineering Electronics Chroma key |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname Electronics, Vol 10, Iss 3113, p 3113 (2021) Electronics; Volume 10; Issue 24; Pages: 3113 |
| Popis: | There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks. This work was supported by the Spanish Government under projects PID2019-104793RBC31/ AEI/10.13039/501100011033, RTI2018-096036-B-C22/AEI/10.13039/501100011033, TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033, and PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17). |
| Databáze: | OpenAIRE |
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