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Bernhard Jaeger

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Articles

LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving

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By Bernhard Jaeger, Daniel G. Dauner, Micha Fauth, Maximilian Igl
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (652) Issue with article?

End-to-End Autonomous Driving: Challenges and Frontiers

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By LI YI CHEN, Penghao Wu, Kashyap Chitta, Bernhard Jaeger
| IEEE
Loading [a11y]/accessibility-menu.js End-to-End Autonomous Driving: Challenges and Frontiers | IEEE Journals & Magazine | IEEE Xplore Skip to Main Content Abstract: The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle...Show More Metadata Abstract: The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework,...
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