Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Atanas Poibrenski"'
Publikováno v:
ACM SIGAPP Applied Computing Review. 20:5-17
Accurate prediction of the future position of pedestrians in traffic scenarios is required for safe navigation of an autonomous vehicle but remains a challenge. This concerns, in particular, the effective and efficient multimodal prediction of most l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::665977dd328082b25c2dac6da055ba44
https://doi.org/10.1145/3447332.3447333
https://doi.org/10.1145/3447332.3447333
Publikováno v:
SSCI
An accurate and fast prediction of future positions of pedestrians by a self-driving car in critical traffic scenarios remains a challenge. The intention of a pedestrian to cross the street can be influenced by social interactions with another one ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3335f2cd8d068088b4ac161d8013bf5b
https://doi.org/10.1109/ssci47803.2020.9308130
https://doi.org/10.1109/ssci47803.2020.9308130
Publikováno v:
SAC
Accurate prediction of the future position of pedestrians in traffic scenarios is required for safe navigation of an autonomous vehicle but remains a challenge. This concerns, in particular, the effective and efficient multimodal prediction of most l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5eb3cf2950f8e864c9200c40110d79f
https://doi.org/10.1145/3341105.3373877
https://doi.org/10.1145/3341105.3373877
Publikováno v:
SEFAIAS@ICSE
In order to make highly/fully automated driving safe, synthetic training and validation data will be required, because critical road situations are too divers and too rare. A few studies on using synthetic data have been published, reporting a genera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff55cf3bb181fb9aacb878e277759eef
https://doi.org/10.1145/3194085.3194093
https://doi.org/10.1145/3194085.3194093
Publikováno v:
SEFAIAS@ICSE
In this paper, we investigate the link between machine perception and human perception for highly/fully automated driving. We compare the classification results of a camera-based frame-by-frame semantic segmentation model (Machine) with a well-establ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03de9885d457d3187a6dce67b5e10018
https://doi.org/10.1145/3194085.3194092
https://doi.org/10.1145/3194085.3194092