Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Scheel, Oliver"'
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator
Externí odkaz:
http://arxiv.org/abs/2109.13333
Autor:
Bergamini, Luca, Ye, Yawei, Scheel, Oliver, Chen, Long, Hu, Chih, Del Pero, Luca, Osinski, Blazej, Grimmett, Hugo, Ondruska, Peter
In this work, we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of self-driving system performance without relying on expensive and time-con
Externí odkaz:
http://arxiv.org/abs/2105.12332
Autor:
Chen, Long, Platinsky, Lukas, Speichert, Stefanie, Osinski, Blazej, Scheel, Oliver, Ye, Yawei, Grimmett, Hugo, del Pero, Luca, Ondruska, Peter
We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners are very hungry for training data, which is usually collected using vehicles equipped wit
Externí odkaz:
http://arxiv.org/abs/2105.12337
Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-s
Externí odkaz:
http://arxiv.org/abs/2004.11995
Publikováno v:
Robotics and Automation Letters 2020 (RA-L)
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model
Externí odkaz:
http://arxiv.org/abs/2003.10381
Lane change prediction of surrounding vehicles is a key building block of path planning. The focus has been on increasing the accuracy of prediction by posing it purely as a function estimation problem at the cost of model understandability. However,
Externí odkaz:
http://arxiv.org/abs/1903.01246
One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes. Such understanding is needed for planning of maneuvers, especially those that are particularly frequent such as lane changes. While in re
Externí odkaz:
http://arxiv.org/abs/1805.06776
Publikováno v:
In Energy Research & Social Science March 2016 13:116-125
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.