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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Autonomous driving in urban environments: approaches, lessons and challenges

Campbell, Mark, Egerstedt, Magnus, How, Jonathan P., Murray, Richard M.·2010·Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Abstract

The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.

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Metadata

Authors
Campbell, M., Egerstedt, M., How, J. P., & Murray, R. M.
Venue
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Published
2010-10-13
Volume
368
Issue
1928
Pages
4649-4672
ISSN
  • 1364-503X (ISSN-L)
  • 1471-2962
OpenAlex
W2102104695
Cited by
353
References
4
FWCI
22.01
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Autonomous driving in urban environments: approaches, lessons and challenges. https://app.cochat.ai/papers/10.1098/rsta.2010.0110
The paper
Campbell, M., Egerstedt, M., How, J. P., & Murray, R. M. (2010). Autonomous driving in urban environments: approaches, lessons and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rsta.2010.0110

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.