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001-es BibID:	BIBFORM104448
035-os BibID:	(Scopus)85140964193
Első szerző:	Kovács László (informatikus)
Cím:	Sensor design and integration into small sized autonomous vehicle / László Kovács, Dávid Baranyai, Tamás Girászi, Tamás Majoros, Ádám Kovács, Máté Vágner, Dénes Palkovics, Tamás Bérczes
Dátum:	2022
Megjegyzések:	Autonomous vehicles use several different kinds of sensors to get information about the surrounding area. With sensors and artificial intelligence, the autonomous vehicle tries to find the optimal decision as close as possible to the appropriate behavior. Because of the huge amount of data, the usage of modern machine learning and data-driven approaches is necessary. Although computing big data is not easily handled especially onboard a vehicle, the critical mass of the diverse data generated from different sources is essential. In the field of autonomous vehicles, there have not been standards yet, but the range of applied sensors is well-known. Most systems use a combination of cameras, radar, and LIDAR (Light Detection and Ranging) sensors that transmit data to a central computer that detects the environment around the car. Self-driving development could be supported with model-sized self-driving vehicles because of the complexity of the area. The development of autonomous vehicles consists of security, communication, and data processing issues. Mistakes are increasing the risks of potential accidents. The realistic environment which can be simulated or built makes it possible that the learned behavior can be carried across the platforms while the differences in the sizes are not playing an important role in the matter of learning. The previous reason causes the model-size self-driving development to be more cost-effective. In our work, we developed a self-driving model car with different types of sensors. Measurement data from them can be used to improve the self-driving capabilities of the vehicle.
ISBN:	978-1-6654-9653-7
Tárgyszavak:	Műszaki tudományok Informatikai tudományok tanulmány, értekezés könyvrészlet Autonomous vehicle sensor integration speed measurement sensor development AUTONOMOUS VEHICLE DATA COLLECTION self-driving car DAVE model sized autonomous vehicles digital actuator system environmental perception LIDAR camera sensors 3D sensing digital streeing 3D localization
Megjelenés:	2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) / ed. Fazekas István. - p. 171-176. -
További szerzők:	Baranyai Dávid Girászi Tamás Majoros Tamás (1991-) (mérnök, informatikus) Kovács Ádám Vágner Máté Palkovics Dénes Bérczes Tamás (1975-) (informatikus)
Pályázati támogatás:	TKP2020-NKA-04 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM104446
035-os BibID:	(Scopus)85140993499
Első szerző:	Vágner Máté
Cím:	3D Localization and Data Quality Estimation with Marvelmind / Máté Vágner, Dénes Palkovics, László Kovács
Dátum:	2022
Megjegyzések:	Localization and position estimation are crucial tasks in autonomous driving. In addition to the importance of positioning, with good quality position tracking, it is possible to implement sophisticated data collection procedures and use advanced machine learning methods such as reinforced learning. Global navigation satellite systems offer very accurate positioning, but their use is cumbersome or impossible under certain laboratory conditions. In our work, we applied an indoor positioning system, that is integrated into our 1:16 self-driving car.
ISBN:	978-1-6654-9653-7
Tárgyszavak:	Műszaki tudományok Informatikai tudományok előadáskivonat könyvrészlet Data preprocessing Data postprocessing Python Sensor Integration Self-driving vehicles Position estimation Indoor Positioning DAVE
Megjelenés:	2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) / ed. Fazekas István. - p. 302-307. -
További szerzők:	Palkovics Dénes Kovács László (1984-) (informatikus)
Pályázati támogatás:	TKP2020-NKA-04 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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