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pro vyhledávání: '"Tas, Omer Sahin"'
Autor:
Tas, Omer Sahin, Wagner, Royden
Motion forecasting transforms sequences of past movements and environment context into future motion. Recent methods rely on learned representations, resulting in hidden states that are difficult to interpret. In this work, we use natural language to
Externí odkaz:
http://arxiv.org/abs/2406.11624
We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles that learns environment representations via redundancy reduction. Our first type of redundancy reduction is induced by an internal transformer decoder and redu
Externí odkaz:
http://arxiv.org/abs/2306.10840
Autor:
Tas, Omer Sahin, Stiller, Christoph
Motion planners take uncertain information about the environment as an input. The environment information is often quite noisy and has a tendency to contain false positive object detection. State-of-the-art motion planners consider all objects alike,
Externí odkaz:
http://arxiv.org/abs/2002.01254
Autor:
Tas, Omer Sahin, Stiller, Christoph
Publikováno v:
In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Changshu-Suzhou China, 26-30 June 2018, pp. 1171--1178
Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This
Externí odkaz:
http://arxiv.org/abs/1810.13001
Anticipating the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation. We introduce a novel self-supervised pre-training method as well as a transformer model for motion prediction. Our method is based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2344ed04001c7463d0e39d09d3173ead
Akademický článek
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Autor:
Hellmund, Andre-Marcel, Wirges, Sascha, Tas, Omer Sahin, Bandera, Claudio, Salscheider, Niels Ole
Publikováno v:
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC); 2016, p1564-1570, 7p
Publikováno v:
2015 IEEE Intelligent Vehicles Symposium (IV); 2015, p1386-1392, 7p
Akademický článek
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Autor:
Taş, Ömer Şahin
This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/77094