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pro vyhledávání: '"Bernhard, Julian"'
Autor:
Hoffmann, Jasper, Fernandez, Diego, Brosseit, Julien, Bernhard, Julian, Esterle, Klemens, Werling, Moritz, Karg, Michael, Boedecker, Joschka
Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be problematic on embedded devices. Especially, when we need to guarantee fi
Externí odkaz:
http://arxiv.org/abs/2404.18863
Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combin
Externí odkaz:
http://arxiv.org/abs/2207.04418
Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This wor
Externí odkaz:
http://arxiv.org/abs/2107.09918
Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. With Deep Reinforcement Learning, optimal driving strategies for such p
Externí odkaz:
http://arxiv.org/abs/2102.03127
For highly automated driving above SAE level~3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the variety of human driving styles. Such uncertainties can generate ambigu
Externí odkaz:
http://arxiv.org/abs/2102.03119
Autor:
Bernhard, Julian, Knoll, Alois
Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic situations. Yet, their use of single-objective optimality impe
Externí odkaz:
http://arxiv.org/abs/2102.03053
Autor:
Bernhard, Julian, Knoll, Alois
A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted with a variety of behavioral variations and limited knowledge about the
Externí odkaz:
http://arxiv.org/abs/2003.11281
Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations
Externí odkaz:
http://arxiv.org/abs/2003.02604
Autor:
Kessler, Tobias, Bernhard, Julian, Buechel, Martin, Esterle, Klemens, Hart, Patrick, Malovetz, Daniel, Le, Michael Truong, Diehl, Frederik, Brunner, Thomas, Knoll, Alois
Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for the resea
Externí odkaz:
http://arxiv.org/abs/1905.02980
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