WFRF:(Listo Zec Edvin)
Sökning: WFRF:(Listo Zec Edvin) > Recurrent Condition...
- 1 av 13
- Föregående post
- Nästa post
- Till träfflistan
| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 02691naa a2200373 4500 | |
| 001 | oai:DiVA.org:ri-51871 | |
| 003 | SwePub | |
| 008 | 210118s2019 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-518712 URI |
| 024 | 7 | a https://doi.org/10.1109/ITSC.2019.89169992 DOI |
| 040 | a (SwePub)ri | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a kon2 swepub-publicationtype |
| 100 | 1 | a Arnelid, Henriku Zenuity AB, Sweden4 aut |
| 245 | 1 0 | a Recurrent Conditional Generative Adversarial Networks forAutonomous Driving Sensor Modelling |
| 264 | 1 | c 2019 |
| 338 | a print2 rdacarrier | |
| 520 | a Simulation of the real world is a widely researchedtopic in various fields. The automotive industry in particular isvery dependent on real world simulations, since these simulations are needed in order to prove the safety of advance driverassistance systems (ADAS) and autonomous driving (AD). Inthis paper we propose a deep learning based model for simulating the outputs from production sensors used in autonomousvehicles. We introduce an improved Recurrent ConditionalGenerative Adversarial Network (RC-GAN) consisting of Recurrent Neural Networks (RNNs) that use Long Short-TermMemory (LSTM) in both the generator and the discriminatornetworks in order to generate production sensor errors thatexhibit long-term temporal correlations. The network is trainedin a sequence-to-sequence fashion where we condition theoutput from the model on sequences describing the surroundingenvironment. This enables the model to capture spatial andtemporal dependencies, and the model is used to generatesynthetic time series describing the errors in a productionsensor which can be used for more realistic simulations. Themodel is trained on a data set collected from real roads withvarious traffic settings, and yields significantly better results ascompared to previous works. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng |
| 653 | a Time series analysis | |
| 653 | a Generators | |
| 653 | a Gallium nitride | |
| 653 | a Generative adversarial networks | |
| 653 | a Production | |
| 653 | a Hidden Markov models | |
| 653 | a Computational modeling | |
| 700 | 1 | a Zec, Edvin Listou RISE,Datavetenskap4 aut0 (Swepub:ri)edvinze@ri.se |
| 700 | 1 | a Mohammadiha, Nasseru Zenuity AB, Sweden4 aut |
| 710 | 2 | a Zenuity AB, Swedenb Datavetenskap4 org |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-51871 |
| 856 | 4 8 | u https://doi.org/10.1109/ITSC.2019.8916999 |
Till lärosätets databas
- 1 av 13
- Föregående post
- Nästa post
- Till träfflistan
Hitta mer i SwePub
- Av författaren/redakt...
- Arnelid, Henrik
- Zec, Edvin Listo
- Mohammadiha, Nas ...
- Om ämnet
-
- NATURVETENSKAP
- NATURVETENSKAP
- och Data och informa ...
- Av lärosätet
- RISE
Sök utanför SwePub
- Sök vidare i:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - Nationella bibliotekssystem
- LIBRIS.kb.se
